Just In: New Slack Community! Connect with AI engineers building with Confident AI, If you are building with LLMs, at some point someone on your team will ask: "How do we know this is actually good?" If you have ever shipped a model update, watched the eval dashboard go green, and still had no answer when leadership asked whether the product actually got better — this playbook is for you.
It is written for engineers wiring pipelines, PMs defining what good looks like, QA owning regression, and leaders who need defensible ship/no-ship criteria.
What this playbook covers:
- How to think about evaluation so your metrics actually predict outcomes — not just generate green dashboards
- When to use human judgment vs automation, and how to make them work together
- How to set up tracing early so you are not scrambling when something breaks in production
- How evaluation differs depending on whether your product is user-facing or internal, single-turn or conversational
- Where to start with end-to-end vs component-level scoring
- How to set up online evaluation in production with trigger moments
Each page is self-contained. You can read them in order or jump to the one that matches where you are right now.
Why should I read this?
Before this playbook, most teams are in some version of: eval scores exist but nobody references them in a ship decision. Leadership asks "did the product get better?" and the room goes quiet. Quality issues surface through support tickets or user complaints, not through the eval pipeline. The team spends more time debating whether the AI works than improving it.
After this playbook, you have metrics that predict outcomes, not just measure activity. You can answer "did the product get better?" with a number leadership believes. Regressions show up in your pipeline before users find them. The eval suite gets better over time because production traffic feeds back into it.
This playbook is also where we share what we have learned from working with hundreds of teams running evaluation on Confident AI — the patterns that consistently work, the mistakes that keep repeating, and the shortcuts that look fast but cost you later. These are not hypothetical best practices. They are the insights we have gathered from watching teams go from first trace to production-grade evaluation, across voice agents, copilots, document extraction pipelines, and everything in between.