gh-greyhaven-ai-claude-code-config-grey-haven-plugins-core/skills/prompt-engineering/reference/prompt-anti-patterns.md

Prompt Anti-Patterns

Common mistakes in prompt engineering and how to fix them.

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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

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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

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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

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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

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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

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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

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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%

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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

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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

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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

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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%

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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%

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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:

• Clear, specific task
• Relevant context and details
• Target audience specified
• Desired output format
• Examples (if applicable)
• Success criteria
• Structured with delimiters
• Affirmative language
• Appropriate scope
• Requirements stated explicitly

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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]

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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

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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