Eval gates: the missing CI step for AI →
The cornerstone: what an eval gate is, its anatomy, and why AI features need one.
Treat AI quality like code quality: gate it. Wiring evaluation into the pipeline — eval gates, quality ratchets, regression suites, canary releases, and "no fake green" as an actual workflow.
The cornerstone: what an eval gate is, its anatomy, and why AI features need one.
The minimum viable eval gate you can add to CI this week.
Why a regression suite beats a better prompt, every time.
Where a human belongs in an otherwise-automated AI pipeline.
What it looks like when an AI system is held to a real testing standard.
Add a Promptfoo eval gate to CI that blocks a merge when quality drops.
Insert a human-in-the-loop approval step before risky AI actions.
Run an LLM eval suite nightly in GitHub Actions with alerts.
Add self-hosted LLM observability with Langfuse or Phoenix.
Build a ratchet where the last good score becomes the new floor.
Make an eval gate a real deploy blocker in GitHub or Vercel.
How to move AI quality checks earlier, to the pull request.
A practical playbook for canary-releasing AI models and prompts.
How to set service-level objectives for AI features.
A post-mortem template for AI incidents, with a worked example.