prompt-engineering

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.

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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.

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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>

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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>

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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>

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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.

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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: {

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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

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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)

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Anti-Patterns

1. Context Pollution: Overloading with irrelevant info → Fix: Only high-signal, task-relevant context
2. Vague Instructions: Assuming shared understanding → Fix: Make all requirements explicit
3. Too Few Examples: 1-2 insufficient → Fix: Use 3-5 diverse examples
4. Minimal Guidance: Under-specifying causes confusion → Fix: Clear, complete instructions even if verbose
5. Unclear Tool Contracts: Ambiguous purposes/parameters → Fix: Clear descriptions, explicit parameters, single responsibility

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Optimization Workflow

1. 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)

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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>

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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

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Resources

• Anthropic Prompt Engineering Guide
• Context Engineering for Agents
• Interactive Tutorial

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When to Use

Optimizing prompts, enforcing formats, debugging outputs, designing complex prompts, building agents/tools, maximizing performance