How to test AI features in a Lovable app before deploying changes

Lovable AI prompt or model updates should be evaluated for intended improvements and possible regressions before release, because changes to an existing Edge Function can affect live users even when frontend changes remain unpublished. The process uses Braintrust to log production requests, build datasets containing representative cases, known failures, and regression guards, then define separate scorers for quality goals such as summary faithfulness and preserved category accuracy. Builders can explore prompt or model variants in Braintrust Playground, but must validate promising changes in a separate candidate Edge Function through remote evaluations that test the full application path, including authentication, request handling, model calls, response parsing, latency, and errors. Comparing preserved Braintrust experiments for production and candidate endpoints against the same dataset enables review of individual regressions, intended fixes, reliability, and cost before promoting the candidate to production. The approach emphasizes secure non-blocking logging, correctly formatted test inputs, safeguards against side effects, matching experiment configurations, and ongoing expansion of evaluation datasets using newly observed production failures.

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