AI Evals - Prove It's Ready with Abhisek Bose

Your AI passed the demo. Can you prove it's ready for production?

Most AI features look great in a demo and then break in front of real users: wrong answers, invented policy, agents taking the wrong action. Usually nobody can say whether the latest change made things better or worse. Evals are how you find out before your users do.

This ebook is the system I use as an AI Product Manager to measure what an AI product actually does and to turn that evidence into ship or no-ship decisions. It's written for the people who own those decisions, not only for the engineers who write the code.

What's inside

- Why demos lie. Where model benchmarks and traditional QA stop being useful, and what to measure instead.

- Datasets that reflect reality. How to build golden sets from real failures, edge cases and production traffic, then version and govern them.

- Choosing evaluators. When to use code checks, heuristics, LLM-as-a-judge, human review or decision-based evaluators.

- Judges you can trust. How to calibrate LLM judges against human labels so their scores mean something.

- RAG, agents, coding agents and voice AI. What to test for each, with worked examples.

- DeepEval in practice. Building real eval suites, with a cookbook of ready-to-use recipes.

- Production evaluation. CI gates, shadow and canary releases, sampling, monitoring, SLOs and incident response.

- The Eval Operating System. Registries, lineage, eval debt and governance, so evals don't die after launch.

- The AI PM's eval playbook. Turning a PRD into an eval spec and an eval result into a release decision.

- A maturity model and a 30-day plan. Find where your team stands and move up one level in a month.

Who it's for

- AI product managers and product owners who need to sign off on AI releases

- Engineers building LLM, RAG or agent features who need more than "it looked fine when I tested it"

- Engineering and product leaders who want a repeatable quality bar across AI teams

What you get

- Case studies, evaluation matrices, checklists and exercises in every part

- Reusable templates and DeepEval code recipes you can adapt to your own product