Evaluation & Testing
Without evaluation, you cannot tell if your AI system is improving or degrading. This section covers the testing approaches that give you confidence in AI system behavior — from prompt unit tests that catch regressions to red-team tests that find safety failures — and the cost/performance tradeoffs that shape every production decision.
In This Section
Prompt Unit Tests
How to write tests for prompt behavior — test cases, assertions, and running evals as part of a CI-like pipeline.
Regression Testing
Catching quality regressions when you change a prompt, switch models, or update your RAG pipeline — before users notice.
Synthetic Data
Generating test cases with AI when you lack real labelled examples — techniques and the risks of evaluating on synthetic data.
Red-Team & Safety Tests
Systematically testing for safety failures — jailbreaks, prompt injection, harmful outputs — before deployment.
Cost & Performance Tradeoffs
Evaluating model quality vs cost vs latency together — how to make the right model and configuration choice for each use case.