Your AI doesn't crash. It doesn't throw a stack trace or fail a build. It just quietly tells your users the wrong thing — with perfect confidence, polished grammar, and zero remorse. This is the new class of production bug: the kind that looks like success right up until it isn't.
Hallucinations, grounding failures, and prompt injection can't be caught by traditional testing. This session gives engineers a practical evaluation framework to close that gap. Starting from a knowledge assistant that passes all functional tests but quietly fails in production, we'll expose three real failure modes and turn each one into a repeatable eval check.
The demo covers golden dataset construction, pass/fail, rubric, and pairwise scoring patterns, and wiring evals into CI so a quality drop kills the PR before it ships. Tools include DeepEval, RAGAS, and eval patterns in pytest. No PhD required — if you've written a unit test, you're already halfway there.
You'll leave with a four-phase eval strategy and a working template you can drop into your stack this week. Because the question isn't whether your AI will lie. It's whether you catch it first.
Learning Outcomes
- Explain difference between benchmarks and application-level evals
- Implement a lightweight golden dataset and scoring rubric for a real AI feature
- Compare pass/fail, rubric, and pairwise scoring patterns and choose the right one
- Wire eval checks into a CI/CD pipeline so quality regressions break the build