Building a Production Pipeline for Prompt Evaluation and Regression Testing

This article presents a production-ready framework for managing prompt changes in LLM applications. Using prompt repositories, replay datasets, automated evaluators, Phoenix tracing, promotion gates, and canary deployments, the author shows how teams can detect behavioral regressions before users experience them. The central argument is that prompts should be treated as operational artifacts rather than text strings, with the same rigor applied to testing, deployment, observability, and rollback strategies as traditional software releases.