6.2 KiB
Claude 4.x Best Practices
Specific best practices for Claude 4.x models (Opus 4, Sonnet 4.5, Haiku 4).
What's Different in Claude 4.x
Claude 4.x models have been trained for more precise instruction following than previous generations.
Key improvements:
- Better adherence to explicit instructions
- Enhanced attention to details and examples
- Improved thinking capabilities for complex reasoning
- More consistent output formatting
- Better handling of structured data
Best Practice 1: Be Explicitly Clear
Claude 4.x responds especially well to clear, explicit instructions.
Do this:
Write a Python function that validates email addresses.
Requirements:
- Function name: validate_email
- Input: string
- Output: boolean
- Use regex for validation
- Support international domains
- Include docstring with examples
Not this:
Make an email validator in Python
Best Practice 2: Provide Context and Motivation
Explaining why helps Claude 4.x understand goals and deliver targeted responses.
Good:
I'm building a user registration system for a SaaS application.
I need to validate email addresses to ensure users provide legitimate contact info.
Write a robust email validation function in Python that:
[requirements]
Better understanding → Better results
Best Practice 3: Ensure Examples Align
Claude 4.x pays close attention to examples as part of instruction following.
Critical: Examples must perfectly match desired behavior
❌ Misaligned example:
<instructions>
Extract dates in ISO format (YYYY-MM-DD)
</instructions>
<example>
Input: "Meeting on January 5th, 2024"
Output: 01/05/2024 <!-- Wrong format! -->
</example>
✅ Aligned example:
<instructions>
Extract dates in ISO format (YYYY-MM-DD)
</instructions>
<example>
Input: "Meeting on January 5th, 2024"
Output: 2024-01-05
</example>
Best Practice 4: Leverage Thinking Capabilities
Claude 4.x has enhanced thinking capabilities for complex tasks.
Use thinking for:
- Multi-step reasoning
- Complex analysis
- Problem-solving
- Reflection after tool use
Example:
<task>
Analyze this codebase architecture and suggest improvements.
</task>
<instructions>
First, think through the analysis in <thinking> tags:
1. What patterns are being used?
2. What are the strengths?
3. What are the weaknesses?
4. What improvements would have the most impact?
Then provide your recommendations.
</instructions>
<codebase>
[code here]
</codebase>
Best Practice 5: Guide Initial Thinking
You can guide Claude's thinking process for better results.
Example:
Solve this algorithm problem. Before coding, think through:
<thinking_guide>
1. What is the core problem?
2. What data structures would be efficient?
3. What's the time complexity target?
4. Are there edge cases to consider?
</thinking_guide>
Then implement the solution.
Best Practice 6: Use Structured Prompts
Claude 4.x excels with well-organized prompts.
Template:
<role>
You are a senior software engineer reviewing code.
</role>
<task>
Review the following pull request for security vulnerabilities.
</task>
<code>
[code here]
</code>
<focus_areas>
- SQL injection risks
- XSS vulnerabilities
- Authentication issues
- Input validation
</focus_areas>
<output_format>
For each issue found:
1. Severity: Critical/High/Medium/Low
2. Location: File and line number
3. Description: What's wrong
4. Recommendation: How to fix
</output_format>
Best Practice 7: Specify Precision Level
Claude 4.x can adjust precision based on your needs.
For high accuracy:
Analyze this data with high precision. Double-check all calculations.
If uncertain about anything, state your confidence level.
For creative tasks:
Generate creative marketing slogans. Prioritize originality and impact.
Best Practice 8: Test and Iterate
Claude 4.x's consistency makes testing more reliable.
Recommended approach:
-
Create test cases covering:
- Happy path
- Edge cases
- Error conditions
- Boundary values
-
Run prompt against all test cases
-
Identify patterns in failures
-
Refine prompt based on failures
-
Repeat until quality threshold met
Model Selection Guide
Choose the right Claude 4.x model for your use case:
Opus 4
Best for:
- Highest accuracy requirements
- Complex reasoning tasks
- Mission-critical applications
- Creative writing
- In-depth analysis
Example use cases:
- Legal document analysis
- Complex code generation
- Research synthesis
- Strategic planning
Sonnet 4.5
Best for:
- Balance of performance and cost
- Most production applications
- Real-time interactions
- General-purpose tasks
Example use cases:
- Chatbots
- Content generation
- Code review
- Data extraction
Haiku 4
Best for:
- Speed-critical applications
- High-volume processing
- Simpler tasks
- Cost optimization
Example use cases:
- Classification
- Simple extraction
- Quick summaries
- Moderation
Performance Optimization
Token Efficiency
Minimize unnecessary tokens:
<!-- Inefficient -->
<instructions>
I would like you to please analyze the following text
and then extract any email addresses that you find
in the text and return them to me in a list format.
</instructions>
<!-- Efficient -->
<instructions>
Extract all email addresses from the text below.
Return as a JSON array.
</instructions>
Batching
For multiple similar tasks:
<instructions>
Classify each review as positive/negative/neutral.
</instructions>
<reviews>
<review id="1">[text]</review>
<review id="2">[text]</review>
<review id="3">[text]</review>
</reviews>
<output_format>
[
{"id": "1", "sentiment": "positive"},
{"id": "2", "sentiment": "negative"},
...
]
</output_format>
Production Readiness Checklist
Before deploying prompts in production:
- Tested on diverse inputs
- Edge cases handled
- Output format strictly controlled
- Examples align with instructions
- Context minimized to essentials
- Error handling specified
- Token usage optimized
- Model version specified
- Success metrics defined
- Fallback behavior defined