prompt-engineering

# Prompt Engineering Techniques for getting reliable, high-quality output from LLMs. ## Core Techniques ### System Prompt Design - Define the role, constraints, and output format upfront - Be explicit about what NOT to do - Put the most important instructions first ### Chain-of-Thought Force step-by-step reasoning before the answer: ``` Think through this step by step: 1. First, identify... 2. Then, evaluate... 3. Finally, decide... ``` ### Few-Shot Examples Provide 2-3 examples of input → output pairs before the actual task. Match the exact format you want back. ### Structured Output Request specific formats: ``` Respond with JSON in this exact format: {"decision": "approve|reject", "reason": "string", "confidence": 0.0-1.0} ``` ## Prompt Patterns ### Persona Pattern ``` You are a senior security engineer reviewing code for vulnerabilities. Focus only on security issues. Ignore style, naming, and formatting. ``` ### Constraint Pattern ``` Rules: - Maximum 3 sentences per response - Use only information from the provided context - If uncertain, say "I don't know" instead of guessing ``` ### Verification Pattern ``` After generating your response, verify: 1. Does it answer the original question? 2. Are all claims supported by evidence? 3. Are there any logical contradictions? If any check fails, revise before responding. ``` ### Decomposition Pattern ``` Break this problem into sub-problems: 1. Solve each sub-problem independently 2. Combine the solutions 3. Verify the combined solution ``` ## API Usage Patterns ### Claude API ```bash curl -s https://api.anthropic.com/v1/messages \ -H "x-api-key: $ANTHROPIC_API_KEY" \ -H "anthropic-version: 2023-06-01" \ -H "content-type: application/json" \ -d '{ "model": "claude-sonnet-4-20250514", "max_tokens": 1024, "system": "You are a code reviewer. Only flag security issues.", "messages": [{"role": "user", "content": "Review this code: ..."}] }' | jq '.content[0].text' ``` ### OpenAI API ```bash curl -s https://api.openai.com/v1/chat/completions \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "gpt-4o", "messages": [ {"role": "system", "content": "You are a code reviewer."}, {"role": "user", "content": "Review this code: ..."} ], "response_format": {"type": "json_object"} }' | jq '.choices[0].message.content' ``` ## Prompt Optimization ### Reduce token usage - Remove filler words and redundant instructions - Use examples instead of lengthy descriptions - Put context in system prompt (cached across turns with some providers) ### Improve reliability - Ask the model to output reasoning before conclusions - Use XML tags to delimit sections: `<context>...</context>` - Be specific: "list 5 items" not "list some items" - Provide negative examples: "Do NOT include..." ### Test prompts - Run the same prompt 5 times — if outputs vary wildly, the prompt is ambiguous - Use temperature 0 for deterministic tasks, 0.7+ for creative tasks - Compare model outputs across providers for the same prompt ## Notes - Prompt engineering is empirical — test and iterate, don't theorize. - Long prompts aren't bad if they reduce ambiguity. Clarity > brevity. - System prompts are more reliable than user-message instructions for persistent behavior. - XML tags (`<instructions>`, `<context>`, `<examples>`) help models parse structured prompts. - Cache-friendly prompts: put static content first, dynamic content last.