kylehughes/optimizing-llm-prompts

Optimizing LLM Prompts

Instructions

Follow these steps to create robust and effective prompts for LLMs (specifically Claude).

1. Define the Goal: Clearly identify the desired output and behavior. Be specific about format, tone, and constraints.
2. 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.
3. 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).
4. Add Examples: Provide 1-3 examples of input -> output mapping to "show" the model what you want.
5. 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.