AI Evals refers to systematic evaluation of large models or AI applications. It's not just about asking a few random questions to get a feel, but turning real tasks into test sets, scoring criteria, and regression checks to determine whether a model or application is truly viable.
Why the chat experience doesn't represent quality
Large models are good at "looking reasonable," but online applications care about stability: whether customer service cites correct policies, whether the knowledge base refuses to answer unknown questions, whether agents randomly click buttons, and whether generated content complies with brand and compliance requirements. Relying on manual testing a few rounds can easily miss boundary cases.
What does an EVAL usually contain?
- Test samples: real user issues, historical tickets, typical failure cases.
- Expected behavior: Should answer, refuse, cite sources, or request more information?
- Scoring methods: manual scoring, rule checks, LLM-as-judge, or mixed scoring.
- Regression process: After updating the model, prompts, and retrieval strategies, run it again.
Different applications have different evaluation priorities
RAG applications should be checked whether recalls are correct, whether answers are faithful to the source, and whether citations are verifiable; Agent applications should be checked to ensure the tool call is safe, whether steps can be restored, and whether the application stops after failure; Content generation should consider tone, facts, formatting, and prohibited words. A universal score does not tell the whole story.
Common misconceptions
Don't wait until the day before launch to do Evals, and don't rely solely on public rankings from model vendors as your own tests. Public rankings can demonstrate the model's fundamental capabilities, but your own Eval can show whether it is reliable in business. The earlier failures are accumulated, the easier it is for AI applications to be stably iterated.
