How to build a prompt CI/CD pipeline

Prompt changes can alter application behavior without conventional code deployment safeguards, so the text describes a CI/CD approach for managing them through versioning, evaluation, approval, staged rollout, and monitoring. Using Braintrust, each prompt revision is assigned an immutable version identifier and paired with its model settings, parameters, dataset version, and code commit to make results reproducible across changes authored in Git, a prompt registry, or a Playground. Candidate prompts are evaluated against production baselines using production-derived datasets, deterministic checks for structured requirements, model-based quality scoring, and measurements of latency and cost; critical failures can block promotion, while mixed or secondary results may require human review. Validated versions progress through development and staging to production via environment assignments, with pull-request checks and permissions supporting controlled approvals. Production releases can use canary routing, predefined rollback triggers, and restoration of prior versions if quality degrades. Online scoring, trace-level version attribution, alerts, and feedback of verified production failures into evaluation datasets extend release criteria into live traffic and support continual improvement of future prompt versions.

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