[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"glossary-canary-prompt::en":3,"gloss-cluster-canary-prompt::en":17,"gloss-next-canary-prompt::en":9},{"slug":4,"category":5,"name":6,"definition":7,"meta_desc":8,"faq":9,"schema_markup":9,"related":10},"canary-prompt","mlops","Canary Prompt","A canary prompt is a prompt change released to a small share of live traffic while the rest keeps the old version. Metrics are compared between the two, and the change either graduates to everyone or is rolled back.\n\nIt exists because prompt edits behave like code deploys with worse tests. A wording change that improves your fifty-case evaluation set can degrade a category the set does not cover, and there is no compiler to catch it. Real traffic is a much larger and much less flattering test set.\n\nWhat to compare is the question most teams get wrong. Latency and cost are easy but rarely the risk. Watch task-level outcomes — resolution rate, edit distance between the model's draft and what the user shipped, escalation rate, thumbs-down — and give the canary long enough to accumulate a decision-worthy sample rather than reading noise on day one.\n\nKeep the rollback trivial. Prompts should be data, versioned and swappable without a deploy; a prompt change that needs an engineer and a release window will not be rolled back quickly, which defeats the point of canarying it.","A canary prompt ships a prompt change to a slice of traffic first — the deployment discipline that catches quality regressions your evals missed.",null,[11,14],{"slug":12,"name":13},"prompt-engineering","Prompt Engineering",{"slug":15,"name":16},"shadow-deployment","Shadow Deployment",[18,22,26,29,33,36,39,42,45,48,51,54],{"slug":19,"category":5,"name":20,"updated_at":21},"annotation-guidelines","Annotation Guidelines","2026-08-24T03:30:02+00:00",{"slug":23,"category":5,"name":24,"updated_at":25},"baseline-model","Baseline Model","2026-08-24T02:46:38+00:00",{"slug":27,"category":5,"name":28,"updated_at":25},"batch-inference","Batch Inference",{"slug":30,"category":5,"name":31,"updated_at":32},"champion-challenger","Champion-Challenger (A\u002FB Model Testing)","2026-08-24T02:46:37+00:00",{"slug":34,"category":5,"name":35,"updated_at":25},"class-imbalance","Class Imbalance",{"slug":37,"category":5,"name":38,"updated_at":25},"continuous-batching","Continuous Batching",{"slug":40,"category":5,"name":41,"updated_at":25},"cross-validation","Cross-Validation",{"slug":43,"category":5,"name":44,"updated_at":25},"data-labeling","Data Labeling",{"slug":46,"category":5,"name":47,"updated_at":32},"drift-detection","Drift Detection",{"slug":49,"category":5,"name":50,"updated_at":32},"eval-harness","Eval Harness",{"slug":52,"category":5,"name":53,"updated_at":32},"experiment-tracking","Experiment Tracking",{"slug":55,"category":5,"name":56,"updated_at":25},"explainability","Explainability"]