prompts

# Prompt Engineering (Deep Workflow) Prompts behave like **natural-language programs**: they need **specs**, **tests**, and **version control**—especially in production. ## When to Offer This Workflow **Trigger conditions:** - Prompt or system message change; quality regressions - Structured outputs (JSON), tool use, or RAG grounding requirements - Safety or policy alignment needs **Initial offer:** Use **six stages**: (1) define task & success, (2) constraints & format, (3) few-shot & style, (4) build eval set, (5) iterate with discipline, (6) ship, monitor, regress). Confirm model family and latency budget. --- ## Stage 1: Define Task & Success **Goal:** Clear user-visible outcome and failure modes (hallucination, omission, tone). **Exit condition:** Success rubric in plain language; out-of-scope cases listed. --- ## Stage 2: Constraints & Format **Goal:** Must/must-not rules; output schema (JSON Schema, bullet structure); length limits. ### Practices - Separate system (policy, role) from user (task instance) - Ask model to cite sources when grounding matters --- ## Stage 3: Few-Shot & Style **Goal:** Use examples only when they reduce ambiguity—avoid huge prompt bloat. ### Practices - Diverse examples; avoid overlong negative examples that confuse --- ## Stage 4: Build Eval Set **Goal:** Frozen inputs with expected properties (not always exact text match). ### Practices - Adversarial and multilingual slices if relevant - Regression suite in CI for critical prompts --- ## Stage 5: Iterate With Discipline **Goal:** Change one major variable at a time when debugging quality. ### Practices - Compare with same temperature settings when A/B testing wording - Log prompt version id with outputs in production --- ## Stage 6: Ship, Monitor, Regress **Goal:** Canary prompt changes; watch implicit signals (thumbs, edits, task completion). --- ## Final Review Checklist - [ ] Task and rubric defined - [ ] Constraints and output format explicit - [ ] Eval set versioned; regression path exists - [ ] Iteration log disciplined; prompt versions tracked - [ ] Production monitoring and rollback plan ## Tips for Effective Guidance - Clarity beats cleverness—short explicit instructions often win. - Chain-of-thought: use when reasoning helps; hide chain from end users if needed. - Align with **llm-evaluation** skill for larger harness design. ## Handling Deviations - **Chat** vs **batch**: batch can use stricter structure and lower temperature. - **Multimodal**: specify how image details may be used or ignored.