majiayu000 Optimizing LLM Prompts
Instructions
Follow these steps to create robust and effective prompts for LLMs (specifically Claude).
- Define the Goal: Clearly identify the desired output and behavior. Be specific about format, tone, and constraints.
- Structure with XML: Use XML tags to delineate sections.
<system>: High-level role and identity.<context>: Static background information.<rules>: Specific constraints and instructions.<examples>: Few-shot demonstrations.
- Draft Instructions:
- Use imperative voice ("Do this", not "You should").
- Quantify everything (e.g., "3 sentences" not "concise").
- Use positive framing (what to do, not just what not to do).
- Add Examples: Provide 1-3 examples of input -> output mapping to "show" the model what you want.
- Iterate: Test with edge cases. If the model fails, add a specific rule or example to address that failure mode.
Best Practices Summary
- XML Structure: Essential for Claude to distinguish between instructions and data.
- Chain of Thought: Ask the model to "think step-by-step" before answering complex queries.
- Progressive Disclosure: Don't dump all context; allow the model to request more if needed.
- Input Sanitation: Wrap user input in distinct tags (e.g.,
<user_query>) to prevent prompt injection.
Checklist
- Structure: Are sections clearly separated (XML/Headers)?
- Clarity: Are instructions imperative and quantified?
- Safety: Is there a catch-all override for conflicting user requests?
- Examples: Are there few-shot examples for complex behaviors?
- Context: Is static context separated from dynamic user queries?
- Output: Is the output format explicitly defined (JSON, Markdown, etc.)?
Detailed Guidance
For a deep dive on critical rules, forbidden practices, and optimization patterns, see REFERENCE.md.