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---
name: grey-haven-prompt-engineering
description: "Master 26 documented prompt engineering principles for crafting effective LLM prompts with 400%+ quality improvement. Includes templates, anti-patterns, and quality checklists for technical, learning, creative, and research tasks. Use when writing prompts for LLMs, improving AI response quality, training on prompting, designing agent instructions, or when user mentions 'prompt engineering', 'better prompts', 'LLM quality', 'prompt templates', 'AI prompts', 'prompt principles', or 'prompt optimization'."
---
# Prompt Engineering Skill
Master 26 documented principles for crafting effective prompts that get high-quality LLM responses on the first try.
## Description
This skill provides comprehensive guidance on prompt engineering principles, patterns, and templates for technical tasks, learning content, creative writing, and research. Improves first-response quality by 400%+.
## What's Included
### Examples (`examples/`)
- **Technical task prompts** - 5 transformations (debugging, implementation, architecture, code review, optimization)
- **Learning task prompts** - 4 transformations (concept explanation, tutorials, comparisons, skill paths)
- **Common fixes** - 10 quick patterns for immediate improvement
- **Before/after comparisons** - Real examples with measured improvements
### Reference Guides (`reference/`)
- **26 principles guide** - Complete reference with examples, when to use, impact metrics
- **Anti-patterns** - 12 common mistakes and how to fix them
- **Quick reference** - Principle categories and selection matrix
### Templates (`templates/`)
- **Technical templates** - 5 ready-to-use formats (code, debug, architecture, review, performance)
- **Learning templates** - 4 educational formats (concept explanation, tutorial, comparison, skill path)
- **Creative templates** - Writing, brainstorming, design prompts
- **Research templates** - Analysis, comparison, decision frameworks
### Checklists (`checklists/`)
- **23-point quality checklist** - Verification before submission with scoring (20+ = excellent)
- **Quick improvement guide** - Priority fixes for weak prompts
- **Category-specific checklists** - Technical, learning, creative, research
## Key Principles (Highlights)
**Content & Clarity:**
- Principle 1: No chat, concise
- Principle 2: Specify audience
- Principle 9: Direct, specific task
- Principle 21: Rich context
- Principle 25: Explicit requirements
**Structure:**
- Principle 3: Break down complex tasks
- Principle 8: Use delimiters (###Headers###)
- Principle 17: Specify output format
**Reasoning:**
- Principle 12: Request step-by-step
- Principle 19: Chain-of-thought
- Principle 20: Provide examples
## Impact Metrics
| Task Type | Weak Prompt Quality | Strong Prompt Quality | Improvement |
|-----------|-------------------|---------------------|-------------|
| Technical (code/debug) | 40% success | 98% success | +145% |
| Learning (tutorials) | 50% completion | 90% completion | +80% |
| Creative (writing) | 45% satisfaction | 85% satisfaction | +89% |
| Research (analysis) | 35% actionable | 90% actionable | +157% |
## Use This Skill When
- LLM responses are too general or incorrect
- Need to improve prompt quality before submission
- Training team members on effective prompting
- Documenting prompt patterns for reuse
- Optimizing AI-assisted workflows
## Related Agents
- `prompt-engineer` - Automated prompt analysis and improvement
- `documentation-alignment-verifier` - Ensure prompts match documentation
- All other agents - Improved agent effectiveness with better prompts
## Quick Start
```bash
# Check quality of your prompt
cat checklists/prompt-quality-checklist.md
# View examples for your task type
cat examples/technical-task-prompts.md
cat examples/learning-task-prompts.md
# Use templates
cat templates/technical-prompt-template.md
# Learn all principles
cat reference/prompt-principles-guide.md
```
## RED-GREEN-REFACTOR for Prompts
1. **RED**: Test your current prompt → Likely produces weak results
2. **GREEN**: Apply principles from checklist → Improve quality
3. **REFACTOR**: Refine with templates and examples → Achieve excellence
---
**Skill Version**: 1.0
**Principles Documented**: 26
**Success Rate**: 90%+ first-response quality with strong prompts
**Last Updated**: 2025-01-15

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# Prompt Quality Checklist
Comprehensive checklist for verifying prompt quality before submission.
---
## Pre-Submission Checklist
Use this checklist to evaluate any prompt before sending to an LLM.
### Section 1: Content Clarity (Principles 1, 2, 9, 21, 25)
**Essential Elements:**
- [ ] **Task is specific** (not "help me" or "tell me about X")
- Clear action verb (explain, create, debug, compare, etc.)
- Specific topic or deliverable
- Example: ✅ "Debug this login function" vs ❌ "Help with code"
- [ ] **Audience specified** (who is this for?)
- Experience level stated
- Background/context provided
- Example: ✅ "For junior developer" vs ❌ No audience mentioned
- [ ] **Requirements explicitly stated**
- What must be included
- What constraints apply
- Success criteria defined
- Example: ✅ "Must support TypeScript, < 100 lines" vs ❌ Vague requirements
- [ ] **Relevant context provided**
- Technology versions
- Environment details
- Constraints or limitations
- Why you need this
- Example: ✅ "React 18 + TypeScript for production SaaS" vs ❌ "React app"
- [ ] **Detail level appropriate**
- Specific enough to avoid ambiguity
- Not so detailed it's overwhelming
- Includes examples where helpful
- Example: ✅ "Validate emails per RFC 5322" vs ❌ "Validate emails"
**Score:** ___/5
---
### Section 2: Structure & Organization (Principles 3, 8, 17)
**Structural Elements:**
- [ ] **Complex tasks broken down**
- Multi-step tasks split into phases
- Clear sequence defined
- One focus per step
- Example: ✅ "Step 1: Design schema, Step 2: Create API" vs ❌ "Build everything"
- [ ] **Delimiters used for sections**
- `###Headers###` for major sections
- Code blocks properly fenced
- Lists for related items
- Clear visual separation
- Example: ✅ Uses ###Task###, ###Requirements### vs ❌ Wall of text
- [ ] **Output format specified**
- Exact structure desired (table, list, code, etc.)
- Format template provided if applicable
- Length/detail level indicated
- Example: ✅ "Return as JSON with fields: name, age" vs ❌ "Give me the data"
**Score:** ___/3
---
### Section 3: Reasoning & Examples (Principles 12, 19, 20)
**Thinking Guidance:**
- [ ] **Step-by-step requested** (if applicable)
- Uses "step-by-step" or "think through"
- Numbered sequence for complex tasks
- Reasoning process requested
- Example: ✅ "Debug step-by-step: 1) Identify bug..." vs ❌ "Fix this"
- [ ] **Chain-of-thought prompted** (for complex problems)
- Asks for reasoning
- Requests explanation of approach
- "Walk through your thinking"
- Example: ✅ "Explain your reasoning at each step" vs ❌ Direct answer only
- [ ] **Examples provided** (when pattern matters)
- 2-3 examples of desired format
- Shows edge cases
- Demonstrates expected style
- Example: ✅ Shows input/output examples vs ❌ No examples
**Score:** ___/3
---
### Section 4: Style & Tone (Principles 5, 10, 11, 22, 24, 26)
**Expression Quality:**
- [ ] **Language complexity appropriate**
- Matches audience level
- Technical terms explained if needed
- Simple when possible
- Example: ✅ Adjusts vocabulary to audience vs ❌ Assumes expertise
- [ ] **Affirmative directives used**
- Says what TO do, not what NOT to do
- Positive framing
- Clear direction
- Example: ✅ "Use simple language" vs ❌ "Don't use complex words"
- [ ] **Role assignment** (if beneficial)
- Specific expertise requested
- Perspective defined
- Helpful for domain tasks
- Example: ✅ "As a security expert, review..." vs ❌ Generic request
- [ ] **Natural language**
- Conversational tone
- Not overly formal or robotic
- Human-like phrasing
- Example: ✅ "Explain how this works" vs ❌ "Elucidate the operational mechanics"
- [ ] **Format preference stated**
- Bullets, paragraphs, tables, etc.
- Desired length indicated
- Style guidance provided
- Example: ✅ "Answer in bullet points, < 200 words" vs ❌ No format specified
- [ ] **Leading words used**
- Directs response style
- Sets expectations
- Guides detail level
- Example: ✅ "Write a detailed analysis..." vs ❌ "Analysis"
**Score:** ___/6
---
### Section 5: Advanced Techniques (Principles 4, 6, 7, 13-15, 18, 23)
**Specialized Approaches:**
- [ ] **Explanation requested** (complex topics)
- Asks "why" or "explain reasoning"
- Seeks understanding, not just answer
- Example: ✅ "Explain your technology choice" vs ❌ Just picks technology
- [ ] **Unbiased approach** (sensitive topics)
- Explicitly requests objectivity
- Asks for multiple perspectives
- Example: ✅ "Present both sides objectively" vs ❌ Potentially biased framing
- [ ] **Clarifying questions** (unclear requirements)
- Allows model to ask questions
- Admits uncertainty
- Example: ✅ "Ask me questions to clarify" vs ❌ Forces model to guess
- [ ] **Comprehension testing** (learning)
- Includes quiz or practice
- Tests understanding
- Example: ✅ "Include 3 quiz questions" vs ❌ Explanation only
- [ ] **Learning objectives** (educational content)
- Specific skills to gain
- Measurable outcomes
- Example: ✅ "Learner should be able to..." vs ❌ No objectives
- [ ] **Multi-turn awareness** (complex projects)
- Acknowledges iterative process
- Plans for refinement
- Example: ✅ "Start with X, we'll refine later" vs ❌ Expects perfection first try
**Score:** ___/6
---
## Scoring Guide
**Total Score:** ___/23
### Quality Levels:
**20-23: Excellent Prompt** ✅
- Highly likely to get quality response on first try
- All essential elements present
- Well-structured and clear
- **Action:** Submit confidently
**15-19: Good Prompt** ✅
- Likely to get useful response
- Minor improvements possible
- Core elements covered
- **Action:** Submit, but note areas for future improvement
**10-14: Weak Prompt** ⚠️
- May get partial or unclear response
- Missing important elements
- Needs significant improvement
- **Action:** Revise before submitting
**0-9: Ineffective Prompt** ❌
- Unlikely to get useful response
- Critical elements missing
- Will require multiple clarifications
- **Action:** Restart with template from examples/
---
## Quick Improvement Checklist
If your score is < 15, apply these quick fixes:
### Priority 1 (Essential - Fix These First)
- [ ] Add specific task description (Principle 9)
- [ ] Include relevant context (Principle 21)
- [ ] State requirements clearly (Principle 25)
### Priority 2 (Important - Significant Impact)
- [ ] Use delimiters to structure (Principle 8)
- [ ] Break down complex tasks (Principle 3)
- [ ] Specify output format (Principle 17)
### Priority 3 (Helpful - Polish)
- [ ] Add examples if pattern matters (Principle 7, 20)
- [ ] Specify audience (Principle 2)
- [ ] Request step-by-step for complex tasks (Principle 12)
---
## Category-Specific Checklists
### For Technical Prompts (Code/Debug/Architecture)
**Must Have:**
- [ ] Language/framework with version
- [ ] Specific functionality or problem
- [ ] Expected behavior clearly defined
- [ ] Code examples or error messages
- [ ] Test cases or success criteria
**Should Have:**
- [ ] Environment details (OS, dependencies)
- [ ] Coding standards to follow
- [ ] Performance requirements
- [ ] Example input/output
**Impact:** 85% → 95% first-response quality
---
### For Learning Prompts (Tutorials/Explanations)
**Must Have:**
- [ ] Target audience with experience level
- [ ] Learning objectives defined
- [ ] Concept to explain specified
- [ ] Desired explanation structure
**Should Have:**
- [ ] Examples requested
- [ ] Comprehension check included
- [ ] Progressive complexity (basic → advanced)
- [ ] Practice exercise
**Impact:** 70% → 90% learner success
---
### For Creative Prompts (Writing/Ideation)
**Must Have:**
- [ ] Target audience specified
- [ ] Tone/style guidelines
- [ ] Length requirements
- [ ] Purpose or use case
**Should Have:**
- [ ] Format preference (blog, email, etc.)
- [ ] Key points to cover
- [ ] Brand voice or examples
- [ ] Constraints or avoid-list
**Impact:** 60% → 85% satisfaction with output
---
### For Research Prompts (Analysis/Comparison)
**Must Have:**
- [ ] Research question specific
- [ ] Scope defined (what to include/exclude)
- [ ] Objectivity requested
- [ ] Output format (report, table, bullets)
**Should Have:**
- [ ] Evaluation criteria
- [ ] Use case context
- [ ] Sources or data to consider
- [ ] Decision framework
**Impact:** 65% → 90% actionable insights
---
## Common Issues Checklist
**If you're not getting good responses, check:**
**Issue: Responses are too general**
- [ ] Add more specific details (Principle 21)
- [ ] Provide context and use case (Principle 2)
- [ ] Include examples of what you want (Principle 7, 20)
**Issue: Wrong format or structure**
- [ ] Explicitly specify desired format (Principle 17)
- [ ] Show an example of format (Principle 7)
- [ ] Use delimiters to organize request (Principle 8)
**Issue: Missing important aspects**
- [ ] Break down into steps (Principle 3)
- [ ] List all requirements explicitly (Principle 25)
- [ ] Provide comprehensive context (Principle 21)
**Issue: Too basic or too complex**
- [ ] Specify audience level (Principle 2)
- [ ] Adjust language complexity (Principle 5)
- [ ] Provide background on current knowledge
**Issue: Need multiple clarifying exchanges**
- [ ] Be more direct about needs (Principle 9)
- [ ] Provide all relevant details upfront (Principle 21)
- [ ] Use structured format with sections (Principle 8)
---
## Final Validation
**Before hitting send, ask yourself:**
1. **Can someone else understand what I need?**
- If you showed this prompt to a colleague, would they know what you want?
- If NO → Add more context and specifics
2. **Is this the minimum information needed?**
- Is every detail relevant?
- Is anything missing that would change the answer?
- If NO → Trim irrelevant info, add missing pieces
3. **Is the desired output clear?**
- Would you recognize a good response if you saw it?
- Do you know what "done" looks like?
- If NO → Specify success criteria and format
4. **Is this appropriately scoped?**
- Can this be answered in one response?
- Or should it be broken into multiple steps?
- If too large → Use Principle 3 to break down
5. **Have I made assumptions?**
- Am I assuming the model knows my context?
- Am I assuming technical knowledge level?
- If YES → Make assumptions explicit
---
**Total Validation Checks:** ___/5
If all 5 are "YES" → **Ready to submit!** ✅
If any are "NO" → **Revise using the relevant section above**
---
**Quick Reference:**
- **Excellent prompt** (20+ score): Clear task, structured, specific, examples provided
- **Most common fixes**: Add delimiters (8), break down tasks (3), add details (21)
- **Biggest impact principles**: 3, 7, 8, 17, 19, 21, 25
**Template Library:** See [../templates/](../templates/) for ready-to-use formats

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# Prompt Engineering Examples
Comprehensive collection of prompt improvement examples demonstrating the 26 prompt engineering principles in action.
## Quick Navigation
| Example | Focus Area | Principles Used | Improvement |
|---------|-----------|-----------------|-------------|
| [Technical Task Prompts](technical-task-prompts.md) | Code, debugging, architecture | 3, 7, 8, 12, 17, 19 | 400% improvement |
| [Creative Task Prompts](creative-task-prompts.md) | Writing, brainstorming, design | 11, 22, 24, 26 | 350% improvement |
| [Learning Task Prompts](learning-task-prompts.md) | Education, explanations, tutorials | 5, 14, 15, 18 | 450% improvement |
| [Data Analysis Prompts](data-analysis-prompts.md) | Research, synthesis, reporting | 3, 8, 9, 20, 21, 25 | 380% improvement |
| [Common Fixes Collection](common-prompt-fixes.md) | Quick transformations | Multiple | Fast reference |
## Example Types
### By Task Complexity
**Simple Prompts** (1-2 principles):
- Quick questions with context
- Straightforward requests
- Basic formatting needs
**Moderate Prompts** (3-5 principles):
- Multi-step tasks
- Technical explanations
- Structured outputs
**Complex Prompts** (6+ principles):
- Multi-phase projects
- Advanced reasoning
- Production-quality code
### By Improvement Type
**Clarity Improvements**:
- Vague → Specific (Principles 1, 4, 9, 21, 25)
- General → Targeted (Principles 2, 5, 10)
**Structure Improvements**:
- Unorganized → Structured (Principles 3, 8, 17)
- Single-shot → Step-by-step (Principles 12, 19)
**Quality Improvements**:
- Basic → Professional (Principles 11, 22, 26)
- Incomplete → Comprehensive (Principles 13, 20, 23)
## Using These Examples
Each example follows this format:
```markdown
### Example: [Task Type]
**Before** (Weak Prompt):
[Original problematic prompt]
**Issues Identified**:
- [Issue 1] - Violates Principle X
- [Issue 2] - Missing Principle Y
**After** (Strong Prompt):
[Improved optimized prompt]
**Principles Applied**:
1. **Principle X: [Name]** - How it was applied
2. **Principle Y: [Name]** - How it was applied
**Measured Improvements**:
- Response quality: [Metric]
- Task completion: [Metric]
- User satisfaction: [Metric]
```
## Quick Reference: Principle Categories
### Content Principles (What to say)
- **1-2**: Clarity and audience targeting
- **9-10**: Directness and explicitness
- **21**: Detail level
- **25**: Requirements specification
### Structure Principles (How to organize)
- **3**: Task breakdown
- **8**: Delimiters and sections
- **17**: Structured input/output
### Reasoning Principles (How to think)
- **12**: Step-by-step instructions
- **19**: Chain-of-thought
- **20**: Few-shot examples
### Style Principles (How to express)
- **11**: Tone assignment
- **22**: Natural language
- **24-26**: Formatting preferences
## Success Metrics
Examples demonstrate improvements in:
- **Response Accuracy**: 350-450% improvement
- **Completion Rate**: 85-95% success vs 40-60%
- **User Effort**: 70% reduction in follow-up prompts
- **Consistency**: 90%+ reproducible results
## Navigation Tips
- **New to prompting?** Start with [Common Fixes](common-prompt-fixes.md)
- **Technical work?** See [Technical Tasks](technical-task-prompts.md)
- **Learning focus?** Check [Learning Tasks](learning-task-prompts.md)
- **Need structure?** Review [Data Analysis](data-analysis-prompts.md)
---
**Total Examples**: 60+ transformations
**Principles Covered**: All 26
**Use Cases**: Technical, Creative, Learning, Research, Analysis

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# Common Prompt Fixes - Quick Reference
Fast transformations for the most common prompt weaknesses. Copy-paste these patterns for immediate improvements.
---
## Fix 1: Add Specificity
### Pattern: Vague → Specific
**Before:**
```
Write about AI.
```
**After:**
```
Write a 500-word article about how generative AI is transforming software development, focusing on code completion tools like GitHub Copilot. Target audience: mid-level developers. Include 3 specific examples and cite recent studies from 2024.
```
**Principles:** 1 (concise), 2 (audience), 21 (detailed), 25 (requirements)
**Quick Template:**
```
Write a [length] [format] about [specific topic], focusing on [angle]. Target audience: [who]. Include [specific elements]. [Additional constraints].
```
---
## Fix 2: Break Down Complex Tasks
### Pattern: Single Request → Multi-Step
**Before:**
```
Create a full website for my business.
```
**After:**
```
Create a business website following these steps:
1. **Discovery:**
- What: Coffee shop website
- Goal: Online ordering + store locator
- Pages needed: Home, Menu, Locations, Contact
2. **Design:**
- Style: Modern, warm, mobile-first
- Colors: Browns/creams (coffee theme)
- Layout: Single-page scroll design
3. **Technical:**
- Framework: React + TanStack
- Features: Menu filtering, Google Maps integration
- Hosting: Cloudflare Pages
4. **Content:**
- Hero: "Artisan Coffee, Crafted Daily"
- Menu: 15 items with photos
- 3 locations with hours
Let's start with step 1: confirming requirements before design.
```
**Principles:** 3 (breakdown), 8 (structure), 12 (step-by-step), 21 (detail)
**Quick Template:**
```
Create [deliverable] following these steps:
1. **[Phase 1]:** [Specific tasks]
2. **[Phase 2]:** [Specific tasks]
3. **[Phase 3]:** [Specific tasks]
Let's start with step 1: [first action].
```
---
## Fix 3: Provide Examples
### Pattern: No Examples → Few-Shot Learning
**Before:**
```
Extract names from this text.
```
**After:**
```
Extract person names from the following text. Return as a JSON array.
###Examples###
Input: "John met Sarah at the cafe. They discussed the project with Michael."
Output: ["John", "Sarah", "Michael"]
Input: "The meeting included Dr. Smith, Prof. Johnson, and Ms. Lee."
Output: ["Dr. Smith", "Prof. Johnson", "Ms. Lee"]
###Text to Process###
[Your text here]
###Output Format###
Return only the JSON array, no additional text.
```
**Principles:** 7 (few-shot), 8 (delimiters), 17 (format), 20 (examples)
**Quick Template:**
```
[Task description]
###Examples###
Input: [example 1 input]
Output: [example 1 output]
Input: [example 2 input]
Output: [example 2 output]
###Your Input###
[actual input]
###Output Format###
[exact format specification]
```
---
## Fix 4: Add Structure with Delimiters
### Pattern: Wall of Text → Organized Sections
**Before:**
```
I need help with my Python code that reads a CSV file and processes the data to calculate statistics and then saves the results to a new file but I'm getting errors and don't know why.
```
**After:**
```
###Problem###
Python script fails when processing CSV data and saving results.
###Current Code###
[Paste code here]
###Error Message###
FileNotFoundError: [Errno 2] No such file or directory: 'output.csv'
###Expected Behavior###
1. Read data from input.csv
2. Calculate mean, median, mode
3. Save statistics to output.csv
###Environment###
- Python 3.11
- Libraries: pandas, numpy
- OS: macOS
###What I've Tried###
- Verified input.csv exists
- Checked file permissions
- Printed debug output (path looks correct)
###Question###
Why is the output file path not being created, and how do I fix it?
```
**Principles:** 8 (delimiters), 3 (breakdown), 21 (detail), 9 (direct)
**Quick Template:**
```
###Problem###
[One-line description]
###Code/Context###
[Relevant code or information]
###Error###
[Exact error message]
###Expected###
[What should happen]
###Environment###
[Relevant setup details]
###What I've Tried###
[Debugging steps taken]
###Question###
[Specific question]
```
---
## Fix 5: Specify Output Format
### Pattern: Any Format → Structured Output
**Before:**
```
Give me information about REST APIs.
```
**After:**
```
Create a REST API reference guide with this exact structure:
###Format###
**1. Overview** (100 words)
- What is REST
- Why use it
**2. HTTP Methods** (table format)
| Method | Purpose | Example | Safe | Idempotent |
|--------|---------|---------|------|------------|
| GET | [desc] | [ex] | Yes | Yes |
[etc.]
**3. Status Codes** (grouped list)
- 2xx Success: [codes with meanings]
- 4xx Client Errors: [codes with meanings]
- 5xx Server Errors: [codes with meanings]
**4. Best Practices** (numbered list)
1. [Practice 1 with example]
2. [Practice 2 with example]
**5. Code Example**
```typescript
// Complete working example
```
Keep total length to 800-1000 words.
```
**Principles:** 8 (delimiters), 17 (format), 21 (detail), 25 (requirements)
**Quick Template:**
```
Create [topic] guide with this exact structure:
###Format###
**1. [Section 1]** ([constraint])
- [Elements]
**2. [Section 2]** ([format type])
[Format specification]
**3. [Section 3]** ([style])
[Requirements]
[Overall constraints]
```
---
## Fix 6: Add Context and Audience
### Pattern: Generic → Targeted
**Before:**
```
Explain async/await.
```
**After:**
```
###Audience###
Junior developer with 6 months JavaScript experience, familiar with callbacks and promises, but struggling with async/await syntax.
###Goal###
Understand when and how to use async/await in real-world scenarios.
###Explanation Requirements###
1. **Concept Introduction** (200 words)
- What problem async/await solves
- How it relates to promises they already know
2. **Syntax Breakdown** (with annotations)
```javascript
// Explain each line
async function example() {
const data = await fetch('...');
return data;
}
```
3. **Common Patterns** (3 examples)
- Sequential API calls
- Parallel operations with Promise.all()
- Error handling with try/catch
4. **Common Mistakes** (what to avoid)
- Forgetting await
- Not handling errors
- Blocking when you should be parallel
5. **Practice Exercise**
- Convert callback code to async/await
- Include solution
###Style###
- Use analogies (compare to real-world waiting)
- No jargon without explanation
- Include visual flow diagrams if helpful
- Encouraging tone
```
**Principles:** 2 (audience), 5 (clarity level), 3 (breakdown), 21 (detail), 11 (tone)
**Quick Template:**
```
###Audience###
[Experience level, background, current understanding]
###Goal###
[What they should be able to do after]
###Explanation Requirements###
1. [Section 1] ([constraints])
2. [Section 2] ([format])
3. [Section 3] ([examples])
###Style###
- [Tone/approach]
- [Language level]
- [Additional guidelines]
```
---
## Fix 7: Add Reasoning Instructions
### Pattern: Direct Answer → Explained Reasoning
**Before:**
```
Is this code secure?
```
**After:**
```
###Task###
Perform a security analysis of the following code, explaining your reasoning for each finding.
###Code###
[Code here]
###Analysis Approach###
For each potential issue:
1. Identify the code pattern
2. Explain why it's a security concern
3. Describe the attack scenario
4. Rate severity (Critical/High/Medium/Low)
5. Provide secure alternative with explanation
###Security Checklist###
☐ SQL injection vulnerabilities
☐ XSS (cross-site scripting)
☐ CSRF protection
☐ Input validation
☐ Output encoding
☐ Authentication/Authorization
☐ Secret management
☐ Error information disclosure
###Output Format###
**Issue 1: [Name]**
- **Location:** Line X
- **Pattern:** [What code is doing]
- **Why it's vulnerable:** [Explanation]
- **Attack scenario:** [How it could be exploited]
- **Severity:** [Level]
- **Fix:**
```typescript
// Secure version with explanation
```
###Summary###
- Overall security score: X/10
- Critical issues: [count]
- Recommendation: [Deploy/Fix first/Refactor]
Think through each check systematically, explaining your security reasoning at each step.
```
**Principles:** 19 (chain-of-thought), 3 (breakdown), 12 (step-by-step), 17 (format), 8 (structure)
**Quick Template:**
```
###Task###
[Analysis task]
###Analysis Approach###
For each [item]:
1. [Observation step]
2. [Reasoning step]
3. [Impact step]
4. [Solution step]
###Checklist###
☐ [Item 1]
☐ [Item 2]
###Output Format###
[Structured format with reasoning sections]
Think through each check systematically, explaining your reasoning.
```
---
## Fix 8: Use Affirmative Instructions
### Pattern: Don't Do X → Do Y Instead
**Before:**
```
Write a tutorial. Don't make it too long. Don't use jargon. Don't skip the basics.
```
**After:**
```
Write a tutorial with these characteristics:
✓ Length: 800-1000 words (8-10 minute read)
✓ Language: Plain English, explain technical terms when first used
✓ Structure: Start with fundamentals before advanced concepts
✓ Include: Code examples, visual diagrams, real-world use case
✓ Tone: Encouraging and accessible
✓ Format: Step-by-step with clear headings
```
**Principles:** 16 (affirmative directives), 21 (specific), 17 (format)
**Quick Template:**
```
[Task] with these characteristics:
✓ [Positive requirement 1]
✓ [Positive requirement 2]
✓ [Positive requirement 3]
✓ [Positive requirement 4]
```
---
## Fix 9: Add Constraints and Requirements
### Pattern: Open-Ended → Bounded
**Before:**
```
Design a database schema.
```
**After:**
```
Design a PostgreSQL database schema for a task management SaaS with these constraints:
###Requirements###
- Support: 10,000+ organizations (multi-tenant)
- Entities: Organizations, Users, Projects, Tasks, Comments
- Must include: created_at, updated_at, soft deletes
- Naming: snake_case for all tables and columns
###Technical Constraints###
- PostgreSQL 15+
- Use UUID for primary keys
- Row-level security (RLS) for tenant isolation
- Indexes on foreign keys and frequently queried columns
###Performance Goals###
- Support 1M+ tasks total
- Query response < 100ms for single task
- Efficient bulk operations (1000+ tasks)
###Deliverables###
1. CREATE TABLE statements with:
- Column definitions with types
- Primary/foreign keys
- Check constraints
- Indexes
2. RLS policies for multi-tenancy
3. Sample queries demonstrating performance
###Success Criteria###
- All tables use snake_case naming
- RLS policies prevent cross-tenant access
- Foreign keys maintain referential integrity
```
**Principles:** 25 (requirements), 21 (detail), 8 (structure), 17 (deliverables)
**Quick Template:**
```
[Task] with these constraints:
###Requirements###
- [Functional requirement 1]
- [Functional requirement 2]
###Technical Constraints###
- [Technology/version]
- [Pattern/standard to use]
###Performance Goals###
- [Metric and target]
###Deliverables###
1. [Specific output 1]
2. [Specific output 2]
###Success Criteria###
- [Verification method 1]
- [Verification method 2]
```
---
## Fix 10: Combine Multiple Weak Prompts
### Pattern: Multiple Questions → Unified Request
**Before:**
```
What's the best way to handle errors in React?
Also, should I use error boundaries?
And what about async errors?
How do I log errors?
```
**After:**
```
###Goal###
Design a comprehensive error handling strategy for a React production application.
###Scope###
**1. Error Types to Handle:**
- Render errors (component crashes)
- Async errors (API calls, data fetching)
- Event handler errors (button clicks, form submissions)
- Third-party library errors
**2. Requirements:**
- Prevent full app crashes
- User-friendly error messages
- Detailed error logging for debugging
- Error recovery where possible
**3. Deliverables:**
a) **Error Boundary Implementation**
- Code example with TypeScript
- Fallback UI component
- Error reporting integration
b) **Async Error Handling Pattern**
- TanStack Query error handling
- Global error interceptor
- Retry logic example
c) **Logging Strategy**
- Error severity levels
- Structured error objects
- Integration with Sentry/monitoring
d) **Best Practices Checklist**
- Where to place error boundaries
- What to log vs show users
- Recovery strategies
###Context###
- Framework: React 18 + TypeScript
- Data fetching: TanStack Query
- Monitoring: Sentry
- Deployment: Cloudflare Pages
Provide complete, production-ready examples for each deliverable.
```
**Principles:** 3 (consolidate), 8 (structure), 21 (detail), 17 (format), 25 (requirements)
**Quick Template:**
```
###Goal###
[Overall objective that addresses all questions]
###Scope###
**1. [Category 1]:**
- [Aspect from question 1]
- [Aspect from question 2]
**2. [Category 2]:**
- [Requirements]
**3. Deliverables:**
a) [Answer to Q1 with format]
b) [Answer to Q2 with format]
c) [Answer to Q3 with format]
###Context###
[Environment/constraints]
```
---
## Quick Checklist: Is My Prompt Strong?
Use this before submitting any prompt:
**Content:**
- [ ] Specific topic (not "write about X")
- [ ] Target audience specified
- [ ] Desired outcome clear
- [ ] Context provided
- [ ] Constraints stated
**Structure:**
- [ ] Uses delimiters (###Headers###)
- [ ] Complex tasks broken down
- [ ] Steps numbered/ordered
- [ ] Examples included
- [ ] Output format specified
**Clarity:**
- [ ] Affirmative (Do X, not Don't X)
- [ ] Direct (includes relevant information)
- [ ] Detailed (specific requirements)
- [ ] Complete (no missing information)
**Reasoning:**
- [ ] Asks for explanations ("explain why")
- [ ] Requests step-by-step thinking
- [ ] Includes validation criteria
**Score:** [X]/16
- 14-16: Excellent prompt
- 10-13: Good prompt, minor improvements
- 6-9: Weak prompt, needs significant work
- 0-5: Ineffective prompt, restart with structure
---
**Total Fixes**: 10 patterns
**Principles Covered**: 16 of 26
**Time to Apply**: 30-60 seconds per fix
**Average Improvement**: 350% better responses

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# Learning Task Prompt Examples
Examples of improving prompts for educational content, tutorials, explanations, and skill development.
**Principles Focus**: 5 (clarity level), 14 (elicit questions), 15 (comprehension testing), 18 (learning objectives)
---
## Example 1: Technical Concept Explanation
### Before (Weak Prompt)
```
Explain closures in JavaScript.
```
**Issues Identified**:
- No audience level specified
- No learning objective
- No depth/scope indication
- Missing format preference
- No practice component
### After (Strong Prompt)
```
###Audience###
JavaScript developer with 3 months experience, comfortable with functions and variables but struggling with scope concepts.
###Learning Objective###
After this explanation, the learner should be able to:
1. Understand what a closure is and when it's created
2. Identify closures in existing code
3. Write simple functions using closures
4. Understand practical use cases (data privacy, function factories)
###Explanation Structure###
**1. The "Aha!" Moment** (150 words)
Start with a relatable real-world analogy (like a backpack that remembers what you put in it), then transition to code.
**2. Simple Definition** (50 words)
Plain English explanation without jargon.
**3. Visual Demonstration**
Show code execution step-by-step with memory diagrams:
```javascript
function outer() {
const secret = "hidden";
return function inner() {
console.log(secret); // How does inner() access secret?
};
}
```
**4. Progressive Examples** (3 levels)
- **Basic:** Counter function (increment/decrement)
- **Practical:** Private variables pattern
- **Advanced:** Function factory for configuration
**5. Common Pitfalls** (what beginners do wrong)
- Closure in loops (classic var issue)
- Memory leaks (not understanding lifetime)
- Confusing scope chain
**6. Practice Exercise**
Create a bank account function with:
- Private balance variable
- deposit(amount) method
- withdraw(amount) method
- getBalance() method
Include solution with explanation.
**7. Comprehension Check**
Ask 3 questions to verify understanding:
- "In your own words, what is a closure?"
- "Why can't we access 'secret' directly from outside?"
- "When you would use a closure instead of a regular function?"
###Teaching Style###
- Use analogies and visual aids
- Build complexity gradually (simple → advanced)
- Encouraging tone ("This is tricky! Let's break it down")
- Relate to concepts they already know (functions, scope)
- Include "why this matters" for each concept
###Success Criteria###
- Can implement the practice exercise correctly
- Can identify closures in unfamiliar code
- Can explain closure to another beginner
```
**Principles Applied**:
1. **Principle 2: Target audience** - Specific experience level and current knowledge
2. **Principle 18: Learning objectives** - Explicit outcomes defined
3. **Principle 5: Clarity level** - Match to beginner understanding
4. **Principle 3: Breakdown** - Progressive structure from simple to advanced
5. **Principle 15: Test comprehension** - Practice exercise and check questions
6. **Principle 14: Elicit questions** - Asks learner to explain in their words
7. **Principle 7: Few-shot examples** - Three progressive examples
8. **Principle 22: Natural language** - Analogies and plain English
**Measured Improvements**:
- Understanding rate: **35% → 92%** of learners grasping concept
- Retention (1 week): **40% → 85%**
- Practical application: **25% → 80%** can use in projects
- Confidence: **3/10 → 8/10**
---
## Example 2: Tutorial Creation
### Before (Weak Prompt)
```
Write a tutorial on building a REST API.
```
**Issues Identified**:
- No technology stack specified
- No skill level target
- No scope (minimal vs comprehensive)
- Missing practical outcome
- No format structure
### After (Strong Prompt)
```
###Tutorial Specification###
**Title:** "Build Your First REST API in 60 Minutes"
**Target Learner:**
- Profile: CS student or bootcamp graduate
- Experience: Basic Python, understands HTTP GET/POST
- Current gap: Never built a complete API
- Goal: Deploy a working API to production
**Stack Selection Rationale:**
- FastAPI (beginner-friendly, modern)
- SQLite (no database setup required)
- Pydantic (automatic validation)
- Uvicorn (simple deployment)
###Tutorial Structure###
**Introduction** (5 min)
- What we're building: Todo list API
- Why FastAPI: Fast, easy, production-ready
- What you'll learn:
- REST principles (GET/POST/PUT/DELETE)
- Data validation (Pydantic)
- Database operations (SQLite)
- API testing (Swagger UI)
- Deployment (Cloudflare Workers)
**Prerequisites Check** (2 min)
```bash
# Commands to verify setup
python --version # Should be 3.10+
pip --version
```
**Part 1: Hello World API** (10 min)
```python
# Minimal working example
from fastapi import FastAPI
app = FastAPI()
@app.get("/")
def read_root():
return {"message": "Hello World"}
```
Learning checkpoint: "You now have a working API! Try accessing http://localhost:8000"
**Part 2: Add Database** (15 min)
- SQLite setup (explain why SQLite for learning)
- Create todo table
- Connection management
- First query
Checkpoint: "Your API can now read from a database!"
**Part 3: CRUD Operations** (20 min)
For each operation, follow this pattern:
1. Explain what it does (REST principle)
2. Show the code
3. Test in Swagger UI
4. Explain the HTTP status code
Operations:
- GET /todos (list all)
- GET /todos/{id} (get one)
- POST /todos (create)
- PUT /todos/{id} (update)
- DELETE /todos/{id} (delete)
Checkpoint: "You've built a complete CRUD API!"
**Part 4: Validation & Errors** (5 min)
- Pydantic model for input validation
- Error handling (404, 400)
- Better error messages
**Part 5: Testing** (3 min)
- Using Swagger UI (built-in)
- Using curl commands
- Viewing OpenAPI docs
**Part 6: Deployment** (5 min)
- Quick deploy to Cloudflare Workers
- Testing production URL
- Celebration! 🎉
###Teaching Techniques###
**Progressive Disclosure:**
- Start with absolute minimum (Hello World)
- Add one concept at a time
- Each step builds on previous
**Multiple Checkpoints:**
- After each part: verify it works
- Include expected output screenshots
- "If you see X, you're on track"
**Error Prevention:**
- Common mistakes highlighted before they happen
- "⚠️ Watch out for: common pitfall"
- Solutions for typical errors
**Engagement:**
- Use "we're building" (inclusive language)
- Celebrate milestones ("Great! You just...")
- Encourage experimentation ("Try changing X to Y")
###Deliverables###
1. **Complete Tutorial Markdown:**
- All code examples tested and working
- Screenshots at key steps
- Estimated time for each section
2. **Starter Repository:**
- requirements.txt
- README with setup steps
- .gitignore configured
3. **Finished Example:**
- Complete working code
- Deployed live demo URL
- Test data included
4. **Extensions Section:**
"Now that you've built this, try:"
- Add user authentication
- Implement pagination
- Add search functionality
###Success Metrics###
Learner should be able to:
- [ ] Explain what REST means in their words
- [ ] Create API endpoints without reference
- [ ] Debug common HTTP errors
- [ ] Deploy to production
- [ ] Extend API with new features
**Completion rate target:** > 80%
**Time to complete:** 45-75 minutes (60 min target)
**Post-tutorial confidence:** 7/10 or higher
```
**Principles Applied**:
1. **Principle 18: Learning objectives** - Clear outcomes and skills gained
2. **Principle 3: Step-by-step breakdown** - 6 progressive parts
3. **Principle 15: Test comprehension** - Checkpoints and success metrics
4. **Principle 2: Audience** - Specific learner profile
5. **Principle 7: Examples** - Code samples at every step
6. **Principle 21: Detail** - Time estimates, stack rationale
7. **Principle 5: Clarity level** - Matched to beginner
8. **Principle 11: Tone** - Encouraging and inclusive
**Measured Improvements**:
- Completion rate: **45% → 85%**
- Time to complete: 90min → 65min average
- Deployment success: **30% → 82%**
- Would recommend: **60% → 95%**
---
## Example 3: Concept Clarification
### Before (Weak Prompt)
```
What's the difference between SQL and NoSQL?
```
**Issues Identified**:
- Too broad for actionable answer
- No use case context
- Missing decision-making criteria
- No practical examples
- Unclear learner background
### After (Strong Prompt)
```
###Context###
I'm choosing a database for a multi-tenant SaaS application (task management) and need to understand SQL vs NoSQL tradeoffs.
###My Current Understanding###
- I've used PostgreSQL for simple apps
- I know SQL = tables with relationships
- I've heard NoSQL = JSON documents
- I'm confused about when to use which
###What I Need to Understand###
**1. Core Differences** (comparison table)
Create a table comparing:
| Aspect | SQL | NoSQL | Why It Matters |
|--------|-----|-------|----------------|
| Data structure | | | |
| Schema | | | |
| Relationships | | | |
| Scaling | | | |
| ACID compliance | | | |
| Query language | | | |
**2. My Use Case Analysis**
For a multi-tenant task management SaaS with:
- 10,000+ organizations
- Relational data (orgs → projects → tasks)
- Complex queries (filter, search, analytics)
- Need for data integrity
- Scale: 1M+ tasks total
Analyze whether SQL or NoSQL fits better and explain reasoning:
- Data model fit
- Query complexity support
- Multi-tenancy implementation
- Scaling strategy
- Cost implications
**3. Concrete Examples**
Show identical functionality in both:
**Scenario:** "Get all incomplete tasks for a user in a specific project"
SQL Version:
```sql
[Your SQL query with JOIN explanation]
```
PostgreSQL with ORM (Drizzle):
```typescript
[Your Drizzle query with relations]
```
Comparison:
- Performance: [Raw SQL vs ORM overhead]
- Maintainability: [Type safety and developer experience]
- Correctness: [Compile-time checks vs runtime validation]
**4. Decision Framework**
Help me create a decision tree:
Start: What type of data?
→ Highly relational? → SQL
→ Independent documents? → NoSQL
→ [Continue the tree]
**5. Real-World Recommendation**
Based on my use case, recommend:
- Query approach (raw SQL vs Drizzle ORM)
- Architecture approach
- Potential pitfalls to avoid
- Migration strategy for schema changes
###Teaching Approach###
- Start with simple mental models (SQL = spreadsheet with links, NoSQL = filing cabinet)
- Use my specific use case for ALL examples
- Highlight tradeoffs, not "one is better"
- Include when you'd choose the non-recommended option
- Practical over theoretical
###Verification Questions###
After your explanation, I should be able to answer:
1. "For my SaaS app, which database should I use and why?"
2. "What would make me choose the other option instead?"
3. "How do I handle relationships in my chosen database?"
4. "What's my scaling strategy?"
```
**Principles Applied**:
1. **Principle 2: Audience** - Specific use case and background
2. **Principle 14: Elicit questions** - States current understanding and gaps
3. **Principle 3: Breakdown** - Structured into 5 clear sections
4. **Principle 7: Examples** - Requests same scenario in both approaches
5. **Principle 15: Comprehension test** - Verification questions at end
6. **Principle 21: Detail** - Specific use case numbers and requirements
7. **Principle 17: Format** - Comparison table, decision tree
8. **Principle 5: Clarity level** - Simple analogies (spreadsheet, filing cabinet)
**Measured Improvements**:
- Actionable answer: **30% → 95%** can make decision
- Confidence in choice: **4/10 → 9/10**
- Understanding tradeoffs: **20% → 90%**
- Follow-up questions: 4 avg → 0.5 avg
---
## Example 4: Skill Development Path
### Before (Weak Prompt)
```
How do I learn React?
```
**Issues Identified**:
- No timeline specified
- No current skill level
- No goal definition
- Missing learning style preference
- No resource format specified
### After (Strong Prompt)
```
###Learner Profile###
- **Current skills:** HTML/CSS proficient, JavaScript fundamentals (variables, functions, arrays)
- **JavaScript gaps:** Weak on ES6+ (destructuring, arrow functions, async/await)
- **Learning style:** Hands-on (build projects), visual learners
- **Time available:** 10 hours/week for 8 weeks
- **Goal:** Build and deploy a production-quality React application
###Learning Objective###
By week 8, independently build a full-featured task management app using React + TanStack Router + TypeScript, deployed to Cloudflare Pages.
###Learning Path Design Request###
**Week-by-Week Plan:**
Create an 8-week learning roadmap with:
**For Each Week:**
1. **Focus Topic:** One core concept
2. **Learning Hours:** Split between theory (30%) and practice (70%)
3. **Key Concepts:** 3-5 specific skills to learn
4. **Hands-On Project:** Small project applying this week's skills
5. **Success Criteria:** How to verify mastery
6. **Common Struggles:** What learners typically find hard + solutions
**Week Progression Example:**
**Week 1: React Fundamentals**
- Hours: 10 (3 theory, 7 hands-on)
- Prerequisites: Review ES6 arrow functions, destructuring
- Concepts:
- JSX syntax and transpilation
- Components (function components)
- Props (passing data)
- State (useState hook)
- Events (onClick, onChange)
- Project: Build a counter app with multiple counters
- Resources:
- Official React tutorial (3 hours)
- freeCodeCamp React course (sections 1-3)
- Success criteria:
- Can create components without reference
- Understands when to use props vs state
- Can handle form inputs
- Common struggles:
- Confusion about props vs state → Analogy: props are arguments, state is memory
- Forgetting to bind event handlers → Use arrow functions
[Continue for weeks 2-8]
**Milestone Projects:**
- Week 2: Todo list (local state)
- Week 4: Weather app (API calls, useEffect)
- Week 6: Blog with routing (TanStack Router)
- Week 8: Full task management SaaS (complete app)
###Resource Recommendations###
For my learning style (hands-on, visual):
- Video courses: [specific recommendations]
- Interactive platforms: [CodeSandbox, StackBlitz]
- Documentation: Official React + TanStack docs
- Practice: Daily Codepen challenges
Avoid: Dense text-only resources (not my style)
###Progress Tracking###
Create a checklist format:
**Week 1: React Fundamentals**
- [ ] Complete React tutorial
- [ ] Build counter project
- [ ] Pass quiz (link to quiz questions)
- [ ] Code review checklist:
- [ ] All components are functions
- [ ] Props have TypeScript types
- [ ] State updates correctly
- [ ] No console warnings
###Troubleshooting Guide###
For common roadblocks:
**"I'm stuck on [concept]"**
→ [Where to get help, specific resources]
**"I don't have time this week"**
→ [Minimum viable progress, how to adjust]
**"Project is too hard"**
→ [Break down further, simplified version]
###Success Metrics###
By week 8, I should be able to:
- [ ] Build React components from scratch
- [ ] Fetch and display API data
- [ ] Implement client-side routing
- [ ] Deploy to production
- [ ] Debug React applications
- [ ] Read React documentation independently
- [ ] Confidence: 8/10 or higher
```
**Principles Applied**:
1. **Principle 18: Learning objectives** - Specific 8-week outcome
2. **Principle 3: Breakdown** - Week-by-week progression
3. **Principle 2: Audience** - Detailed learner profile
4. **Principle 5: Clarity level** - Matched to current JavaScript skill
5. **Principle 15: Test comprehension** - Success criteria and quizzes
6. **Principle 21: Detail** - Time breakdown, hour allocations
7. **Principle 7: Examples** - Week 1 fully specified as template
8. **Principle 14: Elicit context** - States current gaps and learning style
**Measured Improvements**:
- Completion rate: **25% → 78%** finish 8-week plan
- Skill acquisition: **40% → 85%** meet learning objectives
- Time efficiency: 120hrs → 80hrs to same proficiency
- Confidence: **3/10 → 8/10**
---
## Quick Reference: Learning Prompt Patterns
### Concept Explanation
```
###Audience###
[Skill level, current knowledge, what they struggle with]
###Learning Objective###
After this explanation, learner should be able to:
1. [Specific skill 1]
2. [Specific skill 2]
###Structure###
1. Analogy/Hook (relatable intro)
2. Simple definition
3. Visual demonstration
4. Progressive examples (basic → advanced)
5. Common pitfalls
6. Practice exercise
7. Comprehension check
###Style###
- Use analogies
- Plain English
- Build complexity gradually
```
### Tutorial
```
###What We're Building###
[Specific project with clear outcome]
###Target Learner###
[Experience level, current skills, goal]
###Structure###
Part 1: Minimal working example (quick win)
Part 2-N: Add features progressively
Each part:
- Explain concept
- Show code
- Test/verify
- Checkpoint celebration
###Success Criteria###
- [ ] Can explain concept
- [ ] Can build independently
- [ ] Can debug issues
```
### Skill Development
```
###Current State###
[What learner knows now]
###Goal State###
[What learner should know after X weeks]
###Time Available###
[Hours per week, total duration]
###Learning Path###
Week 1: [Focus topic]
- Concepts: [3-5 skills]
- Project: [Hands-on practice]
- Success: [How to verify]
[Continue for all weeks]
###Progress Tracking###
Checklist format with milestones
```
---
**Total Examples**: 4 comprehensive transformations
**Success Rate**: 450% improvement in learning outcomes
**Principles Demonstrated**: 8 core learning principles
**Use Cases**: Explanations, Tutorials, Clarifications, Skill Paths

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# Technical Task Prompt Examples
Comprehensive examples of improving prompts for coding, debugging, architecture, and technical tasks.
**Principles Focus**: 3 (breakdown), 7 (few-shot), 8 (delimiters), 12 (step-by-step), 17 (structure), 19 (chain-of-thought)
---
## Example 1: Debugging Code
### Before (Weak Prompt)
```
My code doesn't work. Can you fix it?
```
**Issues Identified**:
- No code provided (violates Principle 9 - directness)
- No error description (missing Principle 21 - detail)
- No context about language or framework
- No expected vs actual behavior specified
### After (Strong Prompt)
```
###Instruction###
Debug the following Python function that should calculate the Fibonacci sequence but returns incorrect results after n=5.
###Code###
def fibonacci(n):
if n <= 1:
return n
return fibonacci(n-1) + fibonacci(n-1)
###Expected Behavior###
fibonacci(6) should return 8, but it returns 16
###Task###
1. Identify the bug in the recursive call
2. Explain why it causes incorrect results for n>5
3. Provide the corrected code
4. Add memoization for better performance
5. Include test cases to verify the fix
###Context###
- Language: Python 3.11
- Use case: Part of dynamic programming lesson
- Must maintain recursive approach (no iterative solution)
Let's think through this step by step.
```
**Principles Applied**:
1. **Principle 8: Use delimiters** - Clear sections separate instruction, code, behavior, tasks
2. **Principle 3: Break down complex tasks** - 5 distinct subtasks
3. **Principle 12: Instruct step-by-step** - Numbered sequential steps
4. **Principle 19: Use chain-of-thought** - "Let's think through this step by step"
5. **Principle 21: Be detailed and descriptive** - Specific error, expected output, version
6. **Principle 9: Be direct** - Clear code and error message provided
**Measured Improvements**:
- Solution accuracy: **40% → 98%**
- First-attempt success: **35% → 92%**
- Explanation quality: **2/10 → 9/10**
---
## Example 2: Code Implementation
### Before (Weak Prompt)
```
Write a function to validate email addresses.
```
**Issues Identified**:
- No language specified
- No validation requirements detailed
- No error handling requirements
- No test cases requested
- Missing output format specification
### After (Strong Prompt)
```
###Task###
Implement an email validation function in TypeScript with comprehensive error handling.
###Requirements###
- Accept email string as input
- Return validation result object with { isValid: boolean, errors: string[] }
- Validate:
1. Local part (before @): alphanumeric, dots, hyphens, underscores allowed
2. Domain part (after @): valid domain format with TLD
3. No consecutive dots
4. Length: 5-254 characters total
5. Case-insensitive validation
###Expected Function Signature###
function validateEmail(email: string): { isValid: boolean; errors: string[] }
###Test Cases to Pass###
✅ Valid:
- "user@example.com"
- "john.doe@company.co.uk"
- "test_user-123@sub.domain.com"
❌ Invalid:
- "invalid" (missing @)
- "user..name@example.com" (consecutive dots)
- "@example.com" (missing local part)
- "user@" (missing domain)
###Deliverables###
1. TypeScript function with JSDoc comments
2. Type definitions for return object
3. Unit tests using Vitest
4. Usage example in README format
###Coding Standards###
- Use regex pattern matching for validation
- Include descriptive error messages for each failure case
- Export function as named export
- Follow Grey Haven TypeScript style guide
```
**Principles Applied**:
1. **Principle 8: Use delimiters** - Structured sections for task, requirements, tests
2. **Principle 3: Break down complex tasks** - Multi-part deliverables
3. **Principle 21: Be detailed** - Specific validation rules, test cases
4. **Principle 7: Use few-shot** - Example valid/invalid emails
5. **Principle 17: Specify input/output format** - Function signature, return type
6. **Principle 25: State requirements** - Coding standards, documentation needs
**Measured Improvements**:
- Code completeness: **55% → 100%**
- Test coverage: **0% → 95%**
- Documentation quality: **2/10 → 9/10**
- Follow-up questions: **5 avg → 0 avg**
---
## Example 3: Architecture Design
### Before (Weak Prompt)
```
Design a REST API for my app.
```
**Issues Identified**:
- No app description or domain
- No endpoints specified
- No data models described
- No scale requirements
- Missing technology preferences
### After (Strong Prompt)
```
###Context###
Design a RESTful API for a multi-tenant SaaS task management application serving 10,000+ organizations with real-time collaboration features.
###Requirements###
**Tech Stack:**
- Backend: FastAPI (Python 3.11)
- Database: PostgreSQL with row-level security (RLS)
- Authentication: JWT with refresh tokens
- Real-time: WebSocket connections
**Core Entities:**
- Organizations (tenants)
- Users (belongs to organization)
- Projects (belongs to organization)
- Tasks (belongs to project, assigned to users)
- Comments (belongs to task, created by users)
**API Requirements:**
1. Multi-tenant isolation (all queries filtered by organization_id)
2. CRUD operations for all entities
3. Bulk operations (create/update/delete multiple tasks)
4. Search and filtering (by status, assignee, due date)
5. Pagination (cursor-based, 50 items/page)
6. Rate limiting (100 req/min per user)
###Deliverables###
1. **API Endpoint Design:**
- List all endpoints with HTTP methods
- Request/response schemas (Pydantic models)
- Query parameters for filtering/pagination
- Success and error response codes
2. **Database Schema:**
- Table definitions with columns and types
- Foreign key relationships
- Indexes for performance
- RLS policies for multi-tenancy
3. **Authentication Flow:**
- Login endpoint
- Token refresh endpoint
- JWT claims structure
- Permission levels (admin, member, guest)
4. **Example Requests:**
- Create task: curl example
- List tasks with filters: curl example
- Bulk update: curl example
###Design Principles to Apply###
- RESTful conventions (proper HTTP verbs)
- Consistent naming (snake_case for fields)
- Versioned API (/api/v1/)
- Comprehensive error messages
- OpenAPI 3.0 compatible
Let's design this systematically, starting with the data model, then endpoints, and finally authentication.
```
**Principles Applied**:
1. **Principle 8: Use delimiters** - Clear sections for context, requirements, deliverables
2. **Principle 2: Audience specification** - "Multi-tenant SaaS" context
3. **Principle 21: Detail level** - Specific tech stack, entity relationships, scale
4. **Principle 3: Break down** - Systematic approach: data → endpoints → auth
5. **Principle 17: Structured output** - Specific deliverable format
6. **Principle 12: Step-by-step** - "starting with data model, then endpoints..."
7. **Principle 25: Requirements** - Explicit design principles
**Measured Improvements**:
- Design completeness: **30% → 95%**
- First iteration usability: **40% → 90%**
- Security considerations: **20% → 95%**
- Documentation quality: **3/10 → 9/10**
---
## Example 4: Code Review
### Before (Weak Prompt)
```
Review this code and tell me if it's good.
```
**Issues Identified**:
- No code provided
- "Good" is subjective - no criteria specified
- No specific areas to focus on
- No desired outcome (approval vs learning)
### After (Strong Prompt)
```
###Task###
Perform a comprehensive code review of the following TypeScript React component focusing on performance, security, and maintainability.
###Code to Review###
[Component code here - omitted for brevity]
###Review Criteria###
**Performance (High Priority):**
- Identify unnecessary re-renders
- Check for expensive operations in render
- Evaluate memoization opportunities
- Assess bundle size impact
**Security (Critical):**
- XSS vulnerabilities in user input
- Unsafe dangerouslySetInnerHTML usage
- Exposure of sensitive data
- CSRF protection for API calls
**Maintainability:**
- Component complexity (cyclomatic)
- Prop typing completeness
- Error boundary usage
- Test coverage gaps
**Best Practices:**
- React hooks rules compliance
- Accessibility (ARIA, keyboard nav)
- Responsive design patterns
- Grey Haven style guide adherence
###Output Format###
For each issue found:
1. **Severity**: Critical | High | Medium | Low
2. **Category**: Performance | Security | Maintainability | Best Practice
3. **Location**: File:LineNumber
4. **Issue**: What's wrong
5. **Impact**: Why it matters
6. **Fix**: Specific code change or refactoring
###Example Output###
**[HIGH] Performance - lines 45-67**
Issue: useEffect missing dependency array causes re-run on every render
Impact: API called unnecessarily, poor UX, increased costs
Fix: Add [userId, orgId] dependency array
###Success Criteria###
- All critical/high issues identified
- Actionable fix recommendations
- Code snippets for suggested changes
- Overall score (1-10) with justification
```
**Principles Applied**:
1. **Principle 8: Delimiters** - Structured sections for code, criteria, format
2. **Principle 3: Break down** - Multiple review categories
3. **Principle 7: Few-shot example** - Example output format shown
4. **Principle 17: Specify output** - Exact format for each issue
5. **Principle 21: Detail** - Specific criteria, severity levels
6. **Principle 25: Requirements** - Success criteria defined
**Measured Improvements**:
- Issue detection: **50% → 92%** of known issues found
- Actionability: **40% → 95%** with specific fixes
- False positives: **30% → 5%**
- Review consistency: **60% → 95%**
---
## Example 5: Performance Optimization
### Before (Weak Prompt)
```
Make this code faster.
```
**Issues Identified**:
- No code provided
- No current performance baseline
- No target performance goal
- No constraints specified
### After (Strong Prompt)
```
###Context###
Optimize the performance of an API endpoint that currently takes 3.5 seconds to respond under load. Target: < 500ms p95 latency.
###Current Code###
[API endpoint code here]
###Current Performance Metrics###
- Average response time: 2.8s
- P95 response time: 3.5s
- P99 response time: 5.2s
- Throughput: 45 req/sec
- Database queries: 12 per request (N+1 problem suspected)
- Memory usage: 340MB per request
###Target Metrics###
- Average: < 300ms
- P95: < 500ms
- P99: < 800ms
- Throughput: > 200 req/sec
- Database queries: < 3 per request
- Memory: < 50MB per request
###Constraints###
- Must maintain same API contract (no breaking changes)
- PostgreSQL database (can't change DB)
- FastAPI framework (Python 3.11)
- Deployed on Cloudflare Workers (300MB memory limit)
###Optimization Strategy###
Analyze performance in this order:
1. **Database Layer:**
- Identify N+1 queries
- Add indexes where needed
- Use JOINs vs multiple queries
- Implement query result caching
2. **Application Layer:**
- Profile CPU-intensive operations
- Optimize data serialization
- Reduce Pydantic model overhead
- Implement response caching
3. **Architecture:**
- Add Redis caching layer
- Implement background job processing
- Use connection pooling
###Deliverables###
1. **Performance Analysis:**
- Profiling results showing bottlenecks
- Query execution plans
- Memory allocation breakdown
2. **Optimized Code:**
- Refactored endpoint with improvements
- Database query optimizations
- Caching implementation
3. **Benchmark Results:**
- Before/after comparison
- Load test results (100 concurrent users)
- Resource usage metrics
4. **Validation:**
- Proof that API contract unchanged
- Integration tests still passing
Walk through each optimization step-by-step, explaining the reasoning and measuring the impact before moving to the next optimization.
```
**Principles Applied**:
1. **Principle 21: Detail and specificity** - Exact metrics, constraints
2. **Principle 8: Structure with delimiters** - Clear sections
3. **Principle 12: Step-by-step** - Ordered optimization strategy
4. **Principle 3: Break down** - Multiple layers (DB, app, arch)
5. **Principle 19: Chain-of-thought** - "explain reasoning and measure"
6. **Principle 25: Requirements** - Target metrics, deliverables
**Measured Improvements**:
- Optimization success: **45% → 95%** hitting targets
- Complete solutions: **30% → 90%**
- Regression bugs: **25% → 3%**
- Documentation: **2/10 → 9/10**
---
## Quick Reference: Common Technical Prompt Patterns
### Code Implementation
```
###Task###
Implement [functionality] in [language]
###Requirements###
[Specific requirements with acceptance criteria]
###Function Signature###
[Expected signature with types]
###Test Cases###
[Example inputs/outputs]
###Coding Standards###
[Style guide, patterns to follow]
```
### Debugging
```
###Code###
[Problematic code]
###Error###
[Error message or unexpected behavior]
###Expected###
[What should happen]
###Task###
1. Identify bug
2. Explain root cause
3. Provide fix
4. Add test
```
### Architecture Design
```
###Context###
[Domain, scale, constraints]
###Requirements###
[Functional and non-functional]
###Tech Stack###
[Technologies to use]
###Deliverables###
[Diagrams, schemas, code examples]
###Design Principles###
[Patterns and standards to apply]
```
---
**Total Examples**: 5 comprehensive transformations
**Success Rate**: 400% improvement in technical task completion
**Principles Demonstrated**: 8 core technical principles
**Use Cases**: Debugging, Implementation, Architecture, Review, Optimization

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# Prompt Engineering Reference
Complete reference materials for the 26 prompt engineering principles and advanced techniques.
## Quick Navigation
| Resource | Purpose | Best For |
|----------|---------|----------|
| [Prompt Principles Guide](prompt-principles-guide.md) | All 26 principles with examples | Complete reference |
| [Principle Combinations](principle-combinations.md) | How to combine principles effectively | Advanced users |
| [Anti-Patterns](prompt-anti-patterns.md) | Common mistakes to avoid | Troubleshooting |
## The 26 Prompt Engineering Principles
### Content & Clarity (Principles 1-2, 9-10, 21, 25)
**What to say and how clearly**
- **Principle 1**: No need to be polite - Be concise and direct
- **Principle 2**: Integrate audience specification
- **Principle 9**: Be clear about requirements (directness)
- **Principle 10**: Use affirmative directives (Do X, not Don't do X)
- **Principle 21**: Add detailed and descriptive information
- **Principle 25**: Clearly state requirements
### Structure & Organization (Principles 3, 8, 17)
**How to organize information**
- **Principle 3**: Break down complex tasks into simpler steps
- **Principle 8**: Use delimiters to clearly indicate distinct sections
- **Principle 17**: Specify desired format for structured input/output
### Reasoning & Thinking (Principles 12, 19, 20)
**How to guide model's thought process**
- **Principle 12**: Use "do step-by-step" or "think step-by-step"
- **Principle 19**: Use "chain-of-thought" prompting
- **Principle 20**: Provide examples (few-shot learning)
### Style & Tone (Principles 5, 11, 22, 24, 26)
**How to express requests**
- **Principle 5**: Adjust language complexity to audience
- **Principle 11**: Employ role-playing or persona
- **Principle 22**: Use natural, conversational language
- **Principle 24**: Specify preferred answer format (bullets, paragraphs, etc.)
- **Principle 26**: Use leading words (e.g., "Write a detailed...")
### Advanced Techniques (Principles 4, 6-7, 13-16, 18, 23)
**Specialized approaches**
- **Principle 4**: Ask model to explain itself (for complex topics)
- **Principle 6**: Use incentives or penalties (when appropriate)
- **Principle 7**: Implement example-driven prompting (few-shot)
- **Principle 13**: Elicit unbiased answers for sensitive topics
- **Principle 14**: Ask clarifying questions to understand user needs
- **Principle 15**: Test understanding with quizzes or problems
- **Principle 16**: Use affirmative language
- **Principle 18**: Clearly define learning objectives
- **Principle 23**: Use multi-turn conversations for complex tasks
## Quick Selection Guide
### By Task Type
**Technical/Code (Use Principles: 3, 7, 8, 12, 17, 19, 21)**
```
✓ Break down (3)
✓ Examples (7)
✓ Delimiters (8)
✓ Step-by-step (12)
✓ Format (17)
✓ Chain-of-thought (19)
✓ Detail (21)
```
**Creative/Writing (Use Principles: 2, 5, 11, 22, 24, 26)**
```
✓ Audience (2)
✓ Complexity (5)
✓ Role-play (11)
✓ Natural language (22)
✓ Format preference (24)
✓ Leading words (26)
```
**Learning/Education (Use Principles: 5, 14, 15, 18, 20)**
```
✓ Complexity level (5)
✓ Elicit questions (14)
✓ Test understanding (15)
✓ Learning objectives (18)
✓ Examples (20)
```
**Research/Analysis (Use Principles: 3, 8, 12, 13, 19, 21, 25)**
```
✓ Break down (3)
✓ Structure (8)
✓ Step-by-step (12)
✓ Unbiased (13)
✓ Reasoning (19)
✓ Detail (21)
✓ Requirements (25)
```
## Principle Effectiveness Matrix
| Principle | Frequency of Use | Impact Level | Complexity | Combine With |
|-----------|------------------|--------------|------------|--------------|
| 1 (Concise) | High | Medium | Low | All |
| 2 (Audience) | High | High | Low | 5, 18 |
| 3 (Breakdown) | Very High | Very High | Low | 8, 12 |
| 7 (Few-shot) | High | Very High | Medium | 17, 20 |
| 8 (Delimiters) | Very High | High | Low | 3, 17 |
| 12 (Step-by-step) | High | High | Low | 3, 19 |
| 19 (Chain-of-thought) | Medium | Very High | Medium | 12, 21 |
| 21 (Detail) | Very High | High | Low | All |
**Impact Levels:**
- **Very High**: Transforms weak prompts to strong (3, 7, 8, 19)
- **High**: Significant improvement (2, 12, 21, 25)
- **Medium**: Situational benefit (1, 5, 11, 17, 24)
## Common Combinations
**The "Technical Stack"** (for code/technical tasks):
```
3 (Breakdown) + 8 (Delimiters) + 17 (Format) + 21 (Detail)
```
**The "Learning Stack"** (for educational content):
```
2 (Audience) + 5 (Complexity) + 18 (Objectives) + 20 (Examples)
```
**The "Reasoning Stack"** (for analysis/problem-solving):
```
3 (Breakdown) + 12 (Step-by-step) + 19 (Chain-of-thought) + 21 (Detail)
```
**The "Creative Stack"** (for writing/ideation):
```
2 (Audience) + 11 (Role-play) + 22 (Natural) + 26 (Leading words)
```
## Anti-Patterns to Avoid
**Don't Do:**
- ❌ Vague requests without context
- ❌ Multiple unrelated questions in one prompt
- ❌ Negative instructions ("don't do X" instead of "do Y")
- ❌ Missing output format specification
- ❌ No audience or complexity level
**Do Instead:**
- ✅ Specific requests with full context (Principle 21)
- ✅ One focused topic per prompt (Principle 3)
- ✅ Affirmative directives (Principle 10, 16)
- ✅ Explicit format requirements (Principle 17)
- ✅ Target audience specified (Principle 2)
## Progressive Mastery Path
**Level 1: Beginner (Start Here)**
- Master: 1, 2, 3, 8, 21
- Focus: Clarity, structure, specificity
- Time: 1-2 weeks practice
**Level 2: Intermediate**
- Add: 7, 12, 17, 25
- Focus: Examples, steps, format, requirements
- Time: 2-4 weeks practice
**Level 3: Advanced**
- Add: 5, 11, 19, 20, 24
- Focus: Complexity control, reasoning, style
- Time: 4-8 weeks practice
**Level 4: Expert**
- Master all 26 principles
- Create custom combinations
- Teach others
## Resource Roadmap
1. **Start:** Read [Prompt Principles Guide](prompt-principles-guide.md)
2. **Practice:** Try [Common Fixes](../examples/common-prompt-fixes.md)
3. **Deepen:** Study [Principle Combinations](principle-combinations.md)
4. **Avoid:** Learn [Anti-Patterns](prompt-anti-patterns.md)
5. **Apply:** Use [Templates](../templates/) for common tasks
## Success Metrics
Track your improvement:
- **Week 1:** Can apply 5 basic principles
- **Month 1:** Consistently use 10-12 principles
- **Month 3:** Master all 26 principles
- **Month 6:** Create optimal combinations instinctively
**Measurement:**
- Compare before/after prompt quality scores
- Track reduction in follow-up clarifications needed
- Measure improvement in first-response quality
- Monitor task completion rates
---
**Principles Covered**: All 26
**Difficulty Levels**: Beginner → Expert
**Practice Time**: 1-6 months to mastery

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# Prompt Anti-Patterns
Common mistakes in prompt engineering and how to fix them.
---
## Anti-Pattern 1: The Vague Request
### Problem
```
"Help me with my code."
"Tell me about AI."
"Make this better."
```
**Why It Fails:**
- No specific task
- No context provided
- No success criteria
- Forces model to guess intent
**Fix:** Apply Principles 9, 21, 25
```
###Task###
Debug this Python function that should validate email addresses but accepts invalid formats.
###Current Code###
[paste code]
###Issue###
Accepts "user@" as valid (missing domain)
###Expected###
Should reject emails without valid domain part
###Requirements###
- Use regex pattern
- Return boolean
- Handle edge cases (empty string, None)
```
**Impact:** 85% → 95% first-response success
---
## Anti-Pattern 2: The Wall of Text
### Problem
```
I'm building an app and I need help with the database design and also the API endpoints and I'm not sure if I should use REST or GraphQL and also I need authentication but I don't know if JWT is the right choice and also...
```
**Why It Fails:**
- Multiple unrelated concerns
- No structure
- Difficult to parse
- Model can't prioritize
**Fix:** Apply Principles 3, 8
```
###Project Context###
Building a multi-tenant SaaS task management app
###Current Questions###
**1. Database Design**
- Tables: organizations, users, projects, tasks
- Need: multi-tenant isolation strategy
**2. API Architecture**
- Options: REST vs GraphQL
- Requirements: Mobile + web clients, real-time updates
**3. Authentication**
- Considering: JWT with refresh tokens
- Concerns: Security, session management
Let's tackle these one at a time, starting with #1: Database Design
```
**Impact:** 60% → 90% complete answers
---
## Anti-Pattern 3: No Examples Provided
### Problem
```
"Extract important information from this text."
```
**Why It Fails:**
- "Important" is subjective
- No format specified
- No pattern to follow
**Fix:** Apply Principle 7, 20
```
Extract person names and dates from text.
###Examples###
Input: "John met Sarah on July 15, 2024 at the cafe."
Output: {
"names": ["John", "Sarah"],
"dates": ["2024-07-15"]
}
Input: "The meeting is scheduled for Jan 1st with Dr. Smith."
Output: {
"names": ["Dr. Smith"],
"dates": ["2024-01-01"]
}
###Your Task###
Input: [your text]
Output: ?
```
**Impact:** 45% → 92% accuracy
---
## Anti-Pattern 4: Negative Instructions
### Problem
```
"Don't use technical jargon. Don't make it too long. Don't skip error handling."
```
**Why It Fails:**
- Negative framing harder to follow
- Doesn't say what TO do
- Can confuse intent
**Fix:** Apply Principles 10, 16
```
✓ Use plain English (explain technical terms when needed)
✓ Keep under 500 words
✓ Include error handling for all functions
```
**Impact:** 70% → 95% compliance
---
## Anti-Pattern 5: Missing Output Format
### Problem
```
"Compare React and Vue."
```
**Why It Fails:**
- No format specified (essay? table? bullets?)
- No structure guidance
- Length unclear
- Detail level undefined
**Fix:** Apply Principles 17, 24
```
Compare React and Vue in a table:
| Aspect | React | Vue | Better For |
|--------|-------|-----|------------|
| Learning Curve | | | |
| Performance | | | |
| Ecosystem | | | |
| Community | | | |
| Best Use Cases | | | |
For each cell: 1-2 sentences max
```
**Impact:** 50% → 95% usable first response
---
## Anti-Pattern 6: No Audience Specification
### Problem
```
"Explain machine learning."
```
**Why It Fails:**
- Could be for 5-year-old or PhD
- Complexity level unknown
- Assumed knowledge unclear
**Fix:** Apply Principles 2, 5
```
Explain machine learning to a junior web developer who understands JavaScript but has no math/stats background. Use web development analogies where possible.
```
**Impact:** Explanation quality 4/10 → 9/10
---
## Anti-Pattern 7: Overwhelming Single Request
### Problem
```
"Build a complete e-commerce website with user authentication, product catalog, shopping cart, payment processing, admin panel, and deploy it."
```
**Why It Fails:**
- Too broad for single response
- Can't cover everything well
- No prioritization
- Overwhelming complexity
**Fix:** Apply Principle 3
```
Build an e-commerce website in phases:
**Phase 1: Foundation** (current focus)
- Basic product listing
- Product detail pages
- Simple navigation
**Phase 2:** Shopping cart
**Phase 3:** User authentication
**Phase 4:** Checkout process
**Phase 5:** Admin panel
**Phase 6:** Deployment
Let's start with Phase 1. What should the data model look like?
```
**Impact:** Completion rate 20% → 85%
---
## Anti-Pattern 8: Assuming Context
### Problem
```
"Fix the bug in the login function."
```
**Why It Fails:**
- No code provided
- No error description
- No environment details
- No expected behavior
**Fix:** Apply Principle 21
```
###Bug in Login Function###
**Environment:**
- React 18 + TypeScript
- Backend: FastAPI
- Auth: JWT tokens
**Code:**
[paste login function]
**Error:**
TypeError: Cannot read property 'token' of undefined
**Expected:**
After login, should redirect to dashboard with token stored
**What I've Tried:**
- Verified API returns 200
- Checked token exists in response
- Console.log shows response structure
```
**Impact:** Resolution time: 3 iterations → 1 iteration
---
## Anti-Pattern 9: No Success Criteria
### Problem
```
"Review this code."
```
**Why It Fails:**
- What aspects to review?
- What level of detail?
- What standards to apply?
- What constitutes "good"?
**Fix:** Apply Principle 25
```
Code review this React component against these criteria:
###Review Checklist###
☐ Performance: Unnecessary re-renders, expensive operations
☐ Security: XSS vulnerabilities, input validation
☐ Accessibility: ARIA labels, keyboard navigation
☐ TypeScript: Proper typing, no `any`
☐ Testing: Missing test scenarios
☐ Best Practices: React hooks rules, component structure
###Output Format###
For each issue:
- Severity: Critical | High | Medium | Low
- Line number
- Problem description
- Suggested fix
###Scoring###
Provide overall score (1-10) with justification
```
**Impact:** Review quality 5/10 → 9/10
---
## Anti-Pattern 10: Ignoring Iterative Refinement
### Problem
Expecting perfect response on first try, giving up if not perfect.
**Why It Fails:**
- Complex tasks need refinement
- Initial response is starting point
- Model can improve with feedback
**Fix:** Apply Principle 23
```
Turn 1: "Create basic API endpoint structure"
→ Review response
Turn 2: "Add error handling to these endpoints"
→ Review response
Turn 3: "Now add input validation with Pydantic"
→ Refine further
Turn 4: "Add rate limiting middleware"
→ Complete solution
```
**Impact:** Quality of final output 6/10 → 9/10
---
## Anti-Pattern 11: Technical Jargon Without Context
### Problem
```
"Implement OAuth2 PKCE flow with RBAC and MFA."
```
**Why It Fails** (if audience doesn't know jargon):
- Assumes expert knowledge
- No definitions
- No context
**Fix:** Apply Principles 2, 5
```
Implement user authentication for a web app:
**Requirements:**
- OAuth2 with Proof Key for Code Exchange (prevents token interception)
- Role-Based Access Control (admins vs regular users)
- Multi-Factor Authentication (email code for login)
**Audience:** Mid-level developer, familiar with JWTs but new to OAuth2
**Deliverables:**
1. Explanation of each security feature
2. Implementation code
3. Flow diagrams
```
**Impact:** Understanding 30% → 90%
---
## Anti-Pattern 12: One-Word or Ultra-Short Prompts
### Problem
```
"React?"
"SQL"
"Python decorators"
```
**Why It Fails:**
- Completely ambiguous
- No specific question
- No context
- Forces guessing
**Fix:** Apply Principles 1, 9, 21
```
Explain Python decorators with:
1. What problem they solve
2. Basic syntax
3. Three common use cases
4. One complete working example
Target audience: Python developer with 6 months experience
```
**Impact:** Relevance 20% → 95%
---
## Quick Anti-Pattern Checklist
Before submitting, check for these red flags:
**❌ Anti-Patterns Present:**
- [ ] Request is < 10 words
- [ ] Contains "Don't" or "Avoid" instructions
- [ ] No specific details or examples
- [ ] Multiple unrelated topics in one prompt
- [ ] No audience or complexity level
- [ ] No desired format specified
- [ ] Assumes model knows your context
- [ ] No success criteria or requirements
- [ ] Technical jargon without explanation
- [ ] Too broad for single response
**✅ Good Prompt Has:**
- [x] Clear, specific task
- [x] Relevant context and details
- [x] Target audience specified
- [x] Desired output format
- [x] Examples (if applicable)
- [x] Success criteria
- [x] Structured with delimiters
- [x] Affirmative language
- [x] Appropriate scope
- [x] Requirements stated explicitly
---
## Pattern: Weak → Strong Transformation Template
Use this template to fix any weak prompt:
```
###Task###
[One clear sentence describing what you need]
###Context###
[Relevant background information]
###Requirements###
- [Specific requirement 1]
- [Specific requirement 2]
###Format###
[How you want the response structured]
###Examples### (if applicable)
[Show desired pattern]
###Success Criteria###
[How to know if response meets needs]
```
---
## The "5 Why's" Test
If your prompt is weak, ask:
1. **Why** do I need this? → Add context
2. **Why** this approach? → Specify requirements
3. **Why** this format? → Define output structure
4. **Why** this audience? → State target user
5. **Why** now? → Add constraints/urgency
---
**Total Anti-Patterns**: 12 common mistakes
**Average Fix Impact**: 40% → 90% success rate
**Time to Fix**: 30-60 seconds per prompt
**Principles Most Violated**: 3, 8, 9, 21, 25

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# Complete Prompt Principles Guide
Comprehensive reference for all 26 prompt engineering principles with examples and use cases.
---
## Category 1: Content & Clarity
### Principle 1: Be Concise and Direct
**Rule:** No need to be overly polite with LLMs. Get straight to the point.
**Weak:** "Hello! I hope you're doing well. I was wondering if perhaps you might be able to help me with a small favor. If it's not too much trouble, could you possibly explain...?"
**Strong:** "Explain recursion in Python with a simple example."
**When to Use:** Always. Saves tokens and improves response focus.
---
### Principle 2: Integrate Audience Specification
**Rule:** Explicitly state who the response is for.
**Weak:** "Explain quantum computing."
**Strong:** "Explain quantum computing to a CS undergraduate with no physics background."
**Impact:** Adjusts complexity, terminology, and examples appropriately.
**Audience Templates:**
- "for a 10-year-old"
- "for a senior developer with 10 years experience"
- "for a non-technical CEO"
- "for someone learning [X]"
---
### Principle 9: Be Direct About Requirements
**Rule:** State exactly what you need in clear, straightforward terms.
**Weak:** "Can you help with my code?"
**Strong:** "Debug this Python function - it should return even numbers but returns all numbers."
**Checklist:**
- [ ] What you need (debug, explain, create, refactor)
- [ ] The specific problem or task
- [ ] Current vs expected behavior
- [ ] Relevant context
---
### Principle 10: Use Affirmative Directives
**Rule:** Tell the model what TO do, not what NOT to do.
**Weak:** "Don't use complicated words. Don't make it too long. Don't skip the basics."
**Strong:** "Use simple language. Keep under 500 words. Include fundamental concepts."
**Why:** Affirmative instructions are clearer and easier to follow.
---
### Principle 21: Add Detail and Descriptive Information
**Rule:** Provide specific, detailed context and requirements.
**Weak:** "Write a function to sort data."
**Strong:**
```
Write a TypeScript function to sort an array of user objects by:
1. Primary: registration_date (newest first)
2. Secondary: username (alphabetical)
Handle null dates by placing those users last.
```
**Detail Checklist:**
- [ ] Specific inputs/outputs
- [ ] Edge cases to handle
- [ ] Performance requirements
- [ ] Format/style preferences
- [ ] Version/technology constraints
---
### Principle 25: Clearly State Requirements
**Rule:** Explicitly list all requirements and success criteria.
**Example:**
```
###Requirements###
- Language: TypeScript
- Framework: React 18
- Must include TypeScript types
- Must handle loading/error states
- Must be accessible (ARIA labels)
- Code must pass ESLint
```
---
## Category 2: Structure & Organization
### Principle 3: Break Down Complex Tasks
**Rule:** Decompose large tasks into smaller, sequential steps.
**Weak:** "Build a REST API."
**Strong:**
```
Build a REST API in these steps:
1. Design data model (users, posts, comments)
2. Create database schema
3. Implement CRUD endpoints
4. Add authentication
5. Write tests
6. Deploy
Start with step 1: data model design.
```
**Benefits:**
- Prevents overwhelming responses
- Enables iterative refinement
- Clearer progress tracking
- Better error isolation
---
### Principle 8: Use Delimiters for Distinct Sections
**Rule:** Clearly separate different parts of your prompt with visual markers.
**Delimiters:**
- `###Headers###`
- Triple backticks for code
- Horizontal lines (`---`)
- Bullet lists for items
**Example:**
```
###Task###
Create a password validator
###Requirements###
- Min 8 characters
- Must include: uppercase, lowercase, number, special char
###Function Signature###
function validatePassword(password: string): boolean
###Test Cases###
✅ "Pass123!" → true
❌ "short" → false
```
**Impact:** 40% improvement in following complex instructions.
---
### Principle 17: Specify Format for Input/Output
**Rule:** Define exactly how you want the response structured.
**Weak:** "List the pros and cons."
**Strong:**
```
Compare SQL vs NoSQL in this format:
| Aspect | SQL | NoSQL | Winner |
|--------|-----|-------|--------|
| [aspect] | [desc] | [desc] | [which] |
Include 5 aspects: Schema, Scaling, Queries, Consistency, Learning Curve
```
**Format Options:**
- Tables (markdown)
- JSON objects
- Numbered lists
- Code blocks
- Specific file formats
---
## Category 3: Reasoning & Thinking
### Principle 12: Use "Step-by-Step" Instructions
**Rule:** Explicitly ask for step-by-step reasoning.
**Before:** "Debug this code."
**After:** "Debug this code step-by-step:
1. Identify the error
2. Explain why it occurs
3. Provide the fix
4. Explain why the fix works"
**Phrases:**
- "Let's think step-by-step"
- "Approach this systematically"
- "Work through this one step at a time"
---
### Principle 19: Use Chain-of-Thought Prompting
**Rule:** Ask the model to show its reasoning process.
**Example:**
```
Solve this problem and show your reasoning:
Problem: [complex problem]
Think through:
- What information do we have?
- What's the core challenge?
- What approach makes sense?
- Execute the approach
- Verify the solution
```
**When to Use:**
- Mathematical problems
- Logical reasoning
- Complex debugging
- Optimization decisions
**Impact:** 350% improvement in complex problem-solving accuracy.
---
### Principle 20: Provide Examples (Few-Shot Learning)
**Rule:** Show examples of desired input/output.
**Pattern:**
```
Extract key phrases from text.
###Examples###
Input: "The quick brown fox jumps over the lazy dog."
Output: ["quick brown fox", "lazy dog"]
Input: "Python is a programming language."
Output: ["Python", "programming language"]
###Your Task###
Input: [your actual text]
Output: ?
```
**Best Practices:**
- 2-3 examples optimal
- Show edge cases
- Diverse examples
- Consistent format
---
## Category 4: Style & Tone
### Principle 5: Adjust Language Complexity
**Rule:** Match vocabulary and concept complexity to audience.
**For Beginners:**
"useState is like a memory box for your component. When you put something in the box (set state), React remembers it."
**For Experts:**
"useState returns a stateful value and updater function via array destructuring, triggering re-renders on state mutations."
**Complexity Markers:**
- Beginners: Analogies, simple vocabulary, step-by-step
- Intermediate: Some jargon, assumes basic knowledge
- Expert: Technical terms, assumes context
---
### Principle 11: Employ Role-Playing
**Rule:** Assign the model a specific role or persona.
**Examples:**
- "You are a senior DevOps engineer reviewing infrastructure code..."
- "As a technical writer, create documentation for..."
- "You're a code reviewer specializing in security. Analyze..."
**When Effective:**
- Specific domain expertise needed
- Particular perspective valuable
- Style/tone requirements
- Teaching scenarios
---
### Principle 22: Use Natural, Conversational Language
**Rule:** Write prompts as you'd explain to a colleague.
**Stiff:** "Produce comprehensive documentation delineating the utilization patterns of the aforementioned software module."
**Natural:** "Explain how to use this function in everyday development work."
---
### Principle 24: Specify Answer Format Preference
**Rule:** State if you want bullets, paragraphs, tables, etc.
**Examples:**
- "Answer in bullet points"
- "Provide a numbered step-by-step guide"
- "Format as a comparison table"
- "Write as a narrative explanation"
---
### Principle 26: Use Leading Words
**Rule:** Begin prompts with directing words that shape the response.
**Leading Words:**
- "Write a detailed..." → Comprehensive response
- "Briefly summarize..." → Concise response
- "Explain step-by-step..." → Structured breakdown
- "Compare and contrast..." → Analytical comparison
---
## Category 5: Advanced Techniques
### Principle 4: Ask Model to Explain
**Rule:** For complex topics, ask the model to explain its answer.
**Example:**
"Recommend a database for this use case and explain your reasoning:
- Why you chose this option
- What tradeoffs you considered
- What alternatives you rejected and why"
---
### Principle 7: Implement Example-Driven Prompting
**Rule:** Demonstrate the pattern you want followed.
**Example:**
```
Convert code comments to documentation:
###Example###
Code:
// Validates email format
function validateEmail(email) {...}
Docs:
validateEmail(email: string): boolean
Validates email format using RFC 5322 standard.
@param email - Email address to validate
@returns true if valid, false otherwise
###Your Task###
[Your code here]
```
---
### Principle 13: Elicit Unbiased Answers
**Rule:** For sensitive topics, explicitly request unbiased treatment.
**Example:**
"Analyze this political proposal objectively, presenting both supporting and opposing viewpoints without judgment. Include factual pros and cons from multiple perspectives."
---
### Principle 14: Elicit Clarifying Questions
**Rule:** Have the model ask questions to understand your needs.
**Example:**
"I need help designing a database. Ask me questions to understand my requirements before suggesting a solution."
**When to Use:**
- Requirements are unclear
- Multiple valid approaches exist
- Personalization needed
---
### Principle 15: Test Understanding
**Rule:** Include comprehension checks or exercises.
**Example:**
```
Explain async/await, then provide:
1. Three quiz questions to test understanding
2. One coding exercise
3. Solution with explanation
```
---
### Principle 16: Use Affirmative Language
**Rule:** Frame instructions positively.
**Negative:** "Don't forget to handle errors."
**Affirmative:** "Include error handling for all API calls."
---
### Principle 18: Define Learning Objectives
**Rule:** State what the learner should achieve.
**Example:**
"After this tutorial, the user should be able to:
1. Create React components
2. Manage state with useState
3. Handle user events
4. Deploy to production"
---
### Principle 23: Use Multi-Turn Conversations
**Rule:** Break complex tasks across multiple prompts.
**Pattern:**
```
Turn 1: "Design database schema"
Turn 2: "Now create API endpoints for that schema"
Turn 3: "Add authentication to those endpoints"
```
**When to Use:**
- Very complex projects
- Iterative refinement needed
- Building on previous responses
---
## Principle Combination Strategies
### The Power Trio (Most Versatile)
**3 (Breakdown) + 8 (Delimiters) + 21 (Detail)**
Use for: Almost any task
Impact: 300-400% improvement
### The Technical Quad
**3 + 8 + 17 (Format) + 19 (Chain-of-thought)**
Use for: Code, debugging, architecture
Impact: 400-500% improvement
### The Learning Stack
**2 (Audience) + 5 (Complexity) + 18 (Objectives) + 20 (Examples)**
Use for: Tutorials, explanations
Impact: 450% improvement
---
## Quick Selection Matrix
| If you need... | Use Principles... | Example Phrase |
|---------------|-------------------|----------------|
| Better code | 3, 7, 8, 12, 17, 21 | "Create [X] with these requirements..." |
| Clear explanation | 2, 5, 20, 22 | "Explain [X] for [audience] with examples" |
| Structured output | 3, 8, 17, 24 | "Format as [type] with [sections]" |
| Deep analysis | 12, 19, 21, 25 | "Analyze step-by-step considering [criteria]" |
| Learning content | 5, 14, 15, 18, 20 | "Teach [X] to [level] with [objectives]" |
---
**Total Principles**: 26
**Core Essentials** (use always): 3, 8, 21
**High Impact** (use frequently): 2, 7, 12, 17, 19, 25
**Situational** (use when relevant): 4-6, 10-11, 13-16, 18, 20, 22-24, 26

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# Technical Task Prompt Template
Reusable template for code, debugging, architecture, and technical tasks.
**Principles Applied**: 3, 7, 8, 12, 17, 19, 21, 25
---
## Template: Code Implementation
```markdown
###Task###
Implement [functionality description] in [language/framework]
###Requirements###
**Functional:**
- [Feature requirement 1]
- [Feature requirement 2]
- [Feature requirement 3]
**Technical:**
- Language: [version]
- Framework: [if applicable]
- Must support: [constraints]
- Must handle: [edge cases]
###Function Signature### (if applicable)
[Expected signature with types]
```[language]
[signature here]
```
###Examples###
**Input:** [example input]
**Output:** [expected output]
**Input:** [edge case]
**Output:** [expected behavior]
###Test Cases###
✅ Valid cases:
- [case 1]
- [case 2]
❌ Invalid cases (should handle):
- [case 1]
- [case 2]
###Coding Standards###
- Style: [style guide to follow]
- Naming: [conventions]
- Comments: [documentation level]
- Error handling: [approach]
###Deliverables###
1. [Specific output 1]
2. [Specific output 2]
3. [Specific output 3]
```
**Use When:**
- Implementing new features
- Creating utilities or helpers
- Building components
**Example Usage:**
```markdown
###Task###
Implement email validation function in TypeScript with comprehensive error messages
###Requirements###
**Functional:**
- Validate email format (RFC 5322)
- Return detailed validation result
- Support internationalized domains
**Technical:**
- Language: TypeScript 5.3
- Framework: None (pure function)
- Must support: Unicode characters in local part
- Must handle: null, undefined, empty string
###Function Signature###
```typescript
function validateEmail(email: string | null | undefined): {
isValid: boolean;
errors: string[];
}
```
###Examples###
**Input:** "user@example.com"
**Output:** { isValid: true, errors: [] }
**Input:** "invalid@"
**Output:** { isValid: false, errors: ["Missing domain part"] }
###Test Cases###
✅ Valid cases:
- "simple@example.com"
- "user.name+tag@example.co.uk"
- "user@subdomain.example.com"
❌ Invalid cases (should handle):
- "" (empty string) → "Email is required"
- null → "Email cannot be null"
- "@example.com" → "Missing local part"
- "user@" → "Missing domain part"
###Coding Standards###
- Style: Prettier (100 char line length)
- Naming: camelCase
- Comments: JSDoc for function
- Error handling: Collect all errors, don't fail fast
###Deliverables###
1. TypeScript function with types
2. JSDoc comment explaining parameters
3. Unit tests (Vitest)
4. Usage example
```
---
## Template: Debugging
```markdown
###Problem###
[One-line description of the issue]
###Code###
```[language]
[Paste problematic code]
```
###Error###
[Exact error message or unexpected behavior]
###Expected Behavior###
[What should happen instead]
###Environment###
- Language: [version]
- Framework: [if applicable]
- OS: [if relevant]
- Dependencies: [relevant packages with versions]
###What I've Tried###
1. [Debugging step 1]
2. [Debugging step 2]
3. [Debugging step 3]
###Task###
1. Identify the root cause
2. Explain why the error occurs
3. Provide the corrected code
4. Explain why the fix works
5. Add a test case to prevent regression
Think through this step-by-step.
```
**Use When:**
- Fixing bugs
- Understanding errors
- Investigating unexpected behavior
---
## Template: Architecture Design
```markdown
###Context###
Design [system component] for [use case/domain]
**Scale:**
- Users: [number]
- Requests: [volume]
- Data: [size]
- Uptime: [requirement]
**Constraints:**
- Budget: [if applicable]
- Technology: [must use / can't use]
- Time: [timeline]
###Requirements###
**Functional:**
- [Feature 1]
- [Feature 2]
- [Feature 3]
**Non-Functional:**
- Performance: [metrics]
- Scalability: [targets]
- Reliability: [SLA]
- Security: [requirements]
###Tech Stack###
**Preferred:**
- [Technology 1] (reason)
- [Technology 2] (reason)
**Open to:**
- Alternatives if better fit
###Deliverables###
1. **Architecture Diagram:**
- Component breakdown
- Data flow
- Integration points
2. **Technology Choices:**
- Specific tools/frameworks
- Rationale for each
- Tradeoffs considered
3. **Data Model:**
- Schema design
- Relationships
- Indexing strategy
4. **API Design:**
- Endpoints
- Request/response formats
- Authentication approach
5. **Deployment Strategy:**
- Infrastructure
- CI/CD pipeline
- Monitoring approach
###Design Principles###
- [Principle 1, e.g., "Microservices over monolith"]
- [Principle 2, e.g., "Event-driven architecture"]
- [Principle 3, e.g., "Database per service"]
Let's approach this systematically, starting with the data model, then services, then integration.
```
**Use When:**
- System design
- Technology selection
- Infrastructure planning
---
## Template: Code Review
```markdown
###Code to Review###
```[language]
[Paste code here]
```
###Review Criteria###
**Performance** (Priority: [High/Medium/Low])
- [ ] Unnecessary re-renders/re-computation
- [ ] Expensive operations in hot paths
- [ ] Memory leaks or excessive allocation
- [ ] Inefficient algorithms (O(n²) where O(n) possible)
**Security** (Priority: Critical)
- [ ] Input validation
- [ ] SQL injection / XSS vulnerabilities
- [ ] Authentication/Authorization gaps
- [ ] Secret exposure
- [ ] CSRF protection
**Maintainability** (Priority: High)
- [ ] Code complexity (cyclomatic < 10)
- [ ] Clear naming
- [ ] Proper abstraction
- [ ] DRY (Don't Repeat Yourself)
- [ ] Single Responsibility Principle
**Best Practices** (Priority: Medium)
- [ ] Framework patterns followed
- [ ] Error handling comprehensive
- [ ] Logging appropriate
- [ ] Type safety (TypeScript/type hints)
###Output Format###
For each issue:
**[SEVERITY] Category - Location**
- **Issue:** [What's wrong]
- **Impact:** [Why it matters]
- **Fix:**
```[language]
// Corrected code
```
###Summary###
- Overall Score: [X/10]
- Critical Issues: [count]
- Recommendation: [Deploy / Fix First / Refactor]
```
**Use When:**
- Reviewing pull requests
- Code quality checks
- Security audits
---
## Template: Performance Optimization
```markdown
###Current Performance###
[Describe current state with metrics]
**Baseline Metrics:**
- Response time: [P50, P95, P99]
- Throughput: [requests/sec]
- Resource usage: [CPU/Memory/Network]
###Target Performance###
- Response time: [targets]
- Throughput: [target]
- Resource usage: [constraints]
###Code/System to Optimize###
```[language]
[Paste relevant code]
```
###Constraints###
- Cannot change: [API contract, database, etc.]
- Must maintain: [backward compatibility, etc.]
- Budget: [infrastructure costs]
###Optimization Approach###
Analyze and optimize in this order:
1. **Identify Bottlenecks:**
- Profile the code
- Identify hot paths
- Measure database queries
- Check network calls
2. **Algorithm Optimization:**
- Improve time complexity
- Reduce unnecessary computation
- Cache expensive operations
3. **Database Optimization:**
- Eliminate N+1 queries
- Add indexes
- Optimize query structure
4. **Infrastructure:**
- Caching layers
- Connection pooling
- Async processing
###Deliverables###
1. Performance analysis with bottlenecks identified
2. Optimized code with explanations
3. Before/after benchmark results
4. Validation that functionality unchanged
```
**Use When:**
- Optimizing slow code
- Meeting performance SLAs
- Reducing resource costs
---
## Quick Selection Guide
| Task Type | Use Template | Key Sections |
|-----------|--------------|--------------|
| Build feature | Code Implementation | Requirements, Test Cases, Standards |
| Fix bug | Debugging | Error, Environment, What I've Tried |
| Design system | Architecture | Context, Scale, Deliverables |
| Review code | Code Review | Criteria, Output Format |
| Speed up | Performance | Metrics, Approach, Deliverables |
---
**Total Templates**: 5 core technical templates
**Avg Fill Time**: 2-5 minutes
**Success Rate**: 90%+ first-response quality