How to evaluate AI app quality with Braintrust and Webflow Cloud

Braintrust can help Webflow Cloud AI applications detect quality regressions that do not produce technical errors by logging production model calls, scoring outputs, and running evaluation suites before changes are deployed. The workflow requires a Webflow Cloud app with AI route handlers, Braintrust and model-provider API keys, and Node.js for locally or CI-run evaluations; logging runs in Cloudflare Workers while evaluations run separately on Node. Route handlers should initialize Braintrust and model clients at request time, record structured inputs, outputs, and metadata, and explicitly await logger.flush() on both successful and failed responses because Workers may terminate before background logs are sent. Evaluation suites combine representative inputs, an AI task, and scorers such as factuality or exact match, enabling teams to compare prompt or model experiments and identify per-case improvements or regressions. Production traces can be promoted into datasets, allowing real user failures to strengthen future test coverage, while CI quality thresholds can block regressions from merging. Common issues include missing production logs from omitted flush calls, Node import errors caused by placing evals in application directories, misleading factuality scores from poorly aligned expected answers, and absent experiments caused by API-key or project-name mismatches.

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