| name | Prompt Engineering |
| description | Anthropic's official prompt engineering best practices. Use when optimizing prompts, debugging outputs, or improving response quality. |
Prompt Engineering
Core Principle: Treat context as finite. Find minimum high-signal tokens for maximum results.
7 Techniques (Priority Order)
1. Be Clear and Direct ⭐
Most impactful. Explicit instructions outperform implicit patterns.
Principles:
- Specify exact output format
- List all constraints explicitly
- Define success criteria upfront
- State critical requirements multiple ways (redundancy improves compliance)
Example:
Good: Extract customer_name, order_id, issue. Format: {"customer_name": "...", "order_id": "...", "issue": "..."}
Bad: Process this ticket.
2. Multishot Prompting (3-5 Examples)
Impact: +30% accuracy (Anthropic studies)
Optimal: 3-5 diverse examples (fewer = insufficient, more = diminishing returns)
Criteria: Relevant, diverse, clear, structured (use XML)
<examples>
<example><input>Find large files</input><output>find . -type f -size +100M</output></example>
<example><input>List all files</input><output>ls -lah</output></example>
<example><input>Count Python files</input><output>find . -name "*.py" | wc -l</output></example>
</examples>
3. Chain of Thought (CoT)
For complex reasoning only. Skip for simple tasks.
Use for: Multi-step analysis, problem-solving, complex reasoning
Skip for: Simple tasks, well-defined operations, direct lookups
<analysis>Your reasoning</analysis>
<final_answer>Your conclusion</final_answer>
4. XML Tags
Semantic structure for complex prompts.
Benefits: Clear boundaries, prevents confusion, enables referencing
Use for: Multi-section prompts, semantic separation
Skip for: Simple single-task prompts
<instructions>Task description</instructions>
<context>Background</context>
<examples>Samples</examples>
<input>Query</input>
<output_format>Expected format</output_format>
5. System Prompts
Assign role or perspective.
- Be specific about expertise domain
- Define constraints/focus areas
- Use for consistent perspective
You are a senior security auditor reviewing code for vulnerabilities.
Focus on: SQL injection, XSS, authentication flaws.
Ignore: Style issues, performance optimizations.
6. Prefilling
Guide output by starting Claude's response.
Use for: Forcing specific format (JSON, CSV), bypassing preambles
User: Generate JSON for this user data
Assistant: {
7. Prompt Chaining
Break complex tasks into sequential steps.
Pattern: Step 1 → output → Step 2 (uses output) → Step 3 (uses output)
Benefits: Clearer results, easier debugging, better accuracy, modular
Context Engineering
From Anthropic's "Effective Context Engineering for AI Agents":
System Prompts: Right altitude (not too specific/vague), structure with <background>, <instructions>, <tools>, <output>, start minimal, add based on failures
Tool Design: Token-efficient returns, minimal overlap, clear purpose, unambiguous parameters
Examples: Curate diverse canonical examples, not exhaustive edge cases
Dynamic Context: Store lightweight identifiers (paths, URLs), load at runtime, enable progressive discovery
Long Tasks: Compaction (summarize near limits), note-taking (persist outside context), sub-agents (specialized, clean context)
Anti-Patterns
- Context Pollution: Overloading with irrelevant info → Fix: Only high-signal, task-relevant context
- Vague Instructions: Assuming shared understanding → Fix: Make all requirements explicit
- Too Few Examples: 1-2 insufficient → Fix: Use 3-5 diverse examples
- Minimal Guidance: Under-specifying causes confusion → Fix: Clear, complete instructions even if verbose
- Unclear Tool Contracts: Ambiguous purposes/parameters → Fix: Clear descriptions, explicit parameters, single responsibility
Optimization Workflow
- Define success → 2. Create tests → 3. Start simple (Technique #1) → 4. Test and measure → 5. Add techniques (#2, #3, #4) → 6. Validate → 7. Stop when satisfied (avoid over-optimization)
Combining Techniques
Layer multiple techniques for best results:
<task>Generate bash commands with explanations</task>
<instructions>Output: command # explanation | No markdown | Start with command</instructions>
<examples>
<example><input>find large files</input><output>find . -type f -size +100M # Find files >100MB</output></example>
<example><input>list files</input><output>ls -lah # List all files with details</output></example>
<example><input>compress PDFs</input><output>tar -czf pdfs.tar.gz *.pdf # Compress all PDFs</output></example>
</examples>
<input>{user_query}</input>
Quick Reference
- Simple tasks: #1 (Be Clear) → Done
- Consistent output: #1 + #2 (3 examples) → Done
- Complex reasoning: #1 + #2 + #3 (CoT) → Test
Rule: Start #1, add techniques in order until quality satisfactory
Resources
When to Use
Optimizing prompts, enforcing formats, debugging outputs, designing complex prompts, building agents/tools, maximizing performance
