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.
LLM-as-Judge Evaluation
Using a model to grade another model's outputs - when it works, when it fails, and how to calibrate it against human judgment.
Hallucination Reduction in Production
Grounding, self-consistency, refusal-by-default, and evaluation metrics for building systems that say "I don't know" when they should.