A prompt is the highest-leverage interface to a foundation model. Effective prompts combine role, instructions, context, examples, output schema, and refusal rules — engineered, evaluated, and version-controlled like any other production code.
Prompt CI/CD: every PR triggers eval against goldens before merge.
Use response_format with JSON schema (OpenAI) or grammars (vLLM). Eliminates parse failures vs free-form text + regex.
Open with role and scope; put long context after. Models attend more strongly to start and end of prompt (lost-in-the-middle).
Even 2-3 examples improve format compliance by 30-50%. Pick examples that cover edge cases, not just happy path.
Store prompts in git with semver. Tie each production trace to a prompt hash for reproducible debugging.
For subjective quality (helpfulness, tone), a stronger model judging weaker outputs correlates well with humans — at 1/100th the cost.
Define what to do when context is missing or instructions conflict. Default-helpful prompts hallucinate; default-cautious prompts ground.
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