ai-prompt-engineering

# Prompt Engineering — Operational Skill Use this skill for production prompt design: schema-first outputs, tool and RAG prompts, prompt hardening, evals, and release workflows. Keep it operational. If the main problem is architecture, retrieval quality, deployment, or inference cost, route to the deeper adjacent skill. ## ASCII Flow ```text prompt job | v pattern choice structured output | extraction | RAG | tool use | rewrite | classify | release | v prompt contract inputs + role/task + context rules + output schema + refusal/failure behavior | v validation schema checks + citation/tool checks + eval cases + regression gate | v released prompt versioned artifact + rollout notes + rollback path ``` ## When to Use This Skill - designing or refactoring prompts for production LLM systems - structured outputs, extraction schemas, or response contracts - prompt debugging, prompt hardening, or prompt review - prompt evals, regression suites, and rollout criteria - tool-use or RAG prompt patterns - multimodal prompts for image, document, audio, or video inputs ## Route Elsewhere - agent architecture and orchestration -> [ai-agents](../ai-agents/SKILL.md) - retrieval quality and chunking -> [ai-rag](../ai-rag/SKILL.md) - broader LLM lifecycle and model strategy -> [ai-llm](../ai-llm/SKILL.md) - inference latency and cost optimization -> [ai-llm-inference](../ai-llm-inference/SKILL.md) - deployment, monitoring, and platform controls -> [ai-mlops](../ai-...