Objective
Stabilize language behavior the way you stabilize APIs: versioned artifacts, schemas, regression suites, and rollback paths. This hub feeds into Prompt engineering as interface design and the prompts theme.
Practice checklist
- Prompt changes carry IDs; diffs are reviewable in the same system as code.
- Structured outputs validated before they reach business logic.
- Canary releases with metrics wired to evaluation.
- Retrieval prompts enforce citation rules coordinated with RAG track.
Learning paths
Newer readers often start here via Path A before evaluation depth. Token and latency trade-offs appear in cost & latency.
Long read
The essay Prompt engineering as interface design covers versioning discipline, structured sections, regression suites, and staged rollbacks when language behavior is part of your API surface.
Review checklist for prompt PRs
- Diff is human-readable; version ID or date is explicit.
- Downstream validators exist for structured outputs; failures are logged, not swallowed.
- Canary or shadow metrics are defined before merge, tied to evaluation gates.
- RAG paths enforce citation IDs from retrieved sets only, aligned with RAG track.
Scope
For how this hub fits the library, see Latest experiments and the prompts as systems theme.
Tool and function definitions
Descriptions exposed to the model are part of the interface: ambiguous tool docs cause wrong calls or spurious invocations. Version them with prompts, and add contract tests for argument shapes and for behavior when tools fail or time out.
Few-shot examples
Examples drift out of date as product copy and policies change. Treat them like fixtures: review on a schedule, align exemplars with safety expectations, and remove shots that no longer match production behavior.
Long context and attachments
When users paste large documents, define truncation or summarization rules explicitly—otherwise the model silently loses the start or end of instructions. Coordinate with cost & latency so limits stay predictable for users and finance.