gh-jamesrochabrun-skills-all-skills/skills/anthropic-prompt-engineer/references/advanced_techniques.md

Advanced Prompt Engineering Techniques

Advanced techniques for optimizing Claude prompts for complex use cases.

Few-Shot Prompting (Learning from Examples)

What it is: Provide examples of inputs and desired outputs to teach Claude the pattern

Why it works: Claude learns from examples better than from abstract instructions alone

When to use:

• Complex output formats
• Specific writing styles
• Pattern recognition tasks
• Consistent formatting needs

Best practices:

• Use 2-5 diverse examples
• Include edge cases
• Ensure examples are accurate
• Use XML tags to structure examples

Example:

<instructions>
Extract product information in the specified format.
</instructions>

<examples>
<example>
<input>iPhone 15 Pro - $999 - 128GB storage, titanium finish</input>
<output>
Product: iPhone 15 Pro
Price: $999
Features: 128GB storage, titanium finish
</output>
</example>

<example>
<input>MacBook Air M2 chip starting at $1,199</input>
<output>
Product: MacBook Air M2
Price: $1,199
Features: M2 chip
</output>
</example>
</examples>

<input>
[new product description]
</input>

---

Role Prompting

What it is: Assign Claude a specific role or persona to guide its responses

Why it works: Provides context for appropriate tone, knowledge level, and approach

When to use:

• Domain-specific tasks
• Specific communication styles
• Educational content
• Professional contexts

Examples:

You are a senior Python developer with 10 years of experience.
Review this code and provide feedback as you would to a junior developer.
Focus on best practices, performance, and maintainability.

<code>
[code here]
</code>

You are a patient elementary school math teacher.
Explain fractions to a 7-year-old using simple language and fun examples.

---

Context Engineering

What it is: Carefully managing what information goes into the prompt

Why it works: LLMs have finite attention - every token counts

Key principles:

• Treat context as a finite resource
• Use "just-in-time" data loading
• Progressive disclosure over dump-all
• Prioritize signal over noise

Best practices:

• Start minimal, add based on failures
• Use structural organization (XML/Markdown headers)
• Remove redundant information
• Find the right altitude (specific but flexible)

Example - Bad:

[Dumps entire 10-page documentation]

Answer this specific question about one feature.

Example - Good:

<instructions>
Answer the user's question using only the relevant documentation below.
</instructions>

<relevant_docs>
[Only the 2 paragraphs about the specific feature]
</relevant_docs>

<question>
How do I configure feature X?
</question>

---

Long-Form Task Prompting

What it is: Breaking complex tasks into clear steps

Why it works: Reduces ambiguity and improves consistency

When to use:

• Multi-step processes
• Complex analysis tasks
• Content generation workflows
• Production systems

Example:

<task>
Generate a comprehensive blog post about topic X.
</task>

<steps>
1. Research: Identify 3 key points about the topic
2. Structure: Create an outline with introduction, 3 main sections, conclusion
3. Write: Develop each section with examples and data
4. Optimize: Add SEO-friendly headers and meta description
5. Review: Check for accuracy, clarity, and completeness
</steps>

<requirements>
- Length: 1500-2000 words
- Tone: Professional but approachable
- Include: Statistics, examples, actionable takeaways
- Audience: Intermediate practitioners
</requirements>

---

Prompt Chaining

What it is: Breaking a complex task into a sequence of simpler prompts

Why it works: Each step focuses on one thing, improving quality

When to use:

• Very complex tasks
• When intermediate verification is needed
• Multi-stage processing
• Quality-critical applications

Example workflow:

1. Analysis prompt: "Extract key themes from this document"
2. Synthesis prompt: "Using these themes, create an outline"
3. Generation prompt: "Using this outline, write the full content"
4. Review prompt: "Review and improve this content"

---

Output Control Techniques

Controlling Length

Specify exact targets:

Write a 250-word summary (aim for 240-260 words)

Use structural limits:

Summarize in exactly 3 bullet points, each 1-2 sentences

Controlling Format

Use prefilling:

Assistant: {
  "status":

Specify structure:

<output_format>
Return markdown with:
## Summary
[2-3 sentences]

## Key Points
- [point 1]
- [point 2]
- [point 3]

## Recommendation
[1 paragraph]
</output_format>

Controlling Tone

Be specific:

Tone: Technical but accessible
- Use industry terminology
- Explain complex concepts simply
- Professional yet conversational
- Avoid jargon where possible

---

Meta-Prompting

What it is: Having Claude help improve or generate prompts

Use cases:

• Generating prompt variations
• Improving existing prompts
• Creating test cases
• Prompt optimization

Example:

I want to create a prompt that extracts structured data from resumes.

Help me design an effective prompt that:
1. Uses XML tags for structure
2. Includes 2 examples
3. Specifies exact JSON output format
4. Handles edge cases (missing information)

Show me the complete prompt.

---

Testing and Iteration

Empirical approach:

1. Start with a baseline prompt
2. Test with diverse inputs
3. Identify failure modes
4. Add examples or constraints
5. Re-test and measure improvement
6. Iterate until quality threshold met

Key metrics:

• Accuracy on test cases
• Consistency across runs
• Edge case handling
• Token efficiency
• Response time

Best practice: Test-driven prompt development

• Create evaluation dataset first
• Define success criteria
• Iterate systematically
• Measure objectively