How do you implement prompt engineering best practices for production LLM applications?
Production prompt engineering requires systematic design, testing, and iteration. Prompt structure: system prompt (role definition, constraints, output format), few-shot examples (demonstrate expected behavior), context (relevant information for the task), and user instruction (the specific request). Design principles: be specific (vague instructions produce vague outputs), provide examples (show don't tell), set constraints explicitly (what to include and exclude), define output format clearly (JSON schema, markdown structure, length limits), and implement guardrails (what the model should refuse to do). Techniques: chain-of-thought (ask the model to reason step by step for complex problems, improves accuracy on math and logic), role prompting (assign a specific expertise role like "You are a senior security engineer"), self-consistency (generate multiple answers and take the majority vote), and tree-of-thought (explore multiple reasoning paths and select the best). Production practices: version control prompts alongside code, implement A/B testing for prompt changes, maintain a prompt regression test suite (set of inputs with expected outputs), log prompt-response pairs for analysis, and implement prompt templates with variable injection (never concatenate user input directly into prompts without sanitization). Optimization: minimize token usage (shorter prompts reduce cost and latency), front-load important instructions (models attend more to the beginning and end), use clear delimiters for different prompt sections, and implement prompt caching for repeated system prompts (reduces cost and latency). Security: implement prompt injection defense (separate system and user content, use input sanitization), detect and block jailbreak attempts, validate outputs before returning to users, and never include secrets in prompts. Evaluation: create evaluation datasets per task, measure accuracy/quality against ground truth, track metrics across prompt versions, and implement automated evaluation where possible.