AI Evaluation

Traditional software is deterministic — same input, same output — so you test it with assertions. An LLM or agent can give a different, plausible-sounding answer every time, which breaks that model. AI evaluation fills the gap: a repeatable way to score outputs against criteria — accuracy, relevance, safety, format, task completion — so you know whether a change made the system better or worse.

In practice it spans offline evals on a fixed test set (catch regressions before you ship) and online evals on live traffic (catch what production surfaces). The hard part is defining “good” for open-ended outputs, which is why methods like LLM-as-a-judge and human review both show up, and why observability and evaluation get used together rather than confused.

It’s a recurring theme on the show because it’s where most AI projects quietly fail: teams ship on vibes, can’t tell why quality drifts, and have no way to improve systematically. Evaluation is the feedback loop that makes iteration possible — without it you’re guessing.