LLM Evals Explained for Product Leaders

LLM evals are automated checks that score your AI feature's outputs against a definition of "good" that you wrote down. Where a unit test asks "did the code run correctly?", an eval asks "was the answer right, grounded, and appropriate?", questions that have no single correct string to compare against. In practice evals come in two kinds: plain code checks for anything objective (did the reply cite a source, was the format valid, how many tokens did it burn), and LLM-as-judge checks, where another model grades the output against a rubric you supply. Product leaders should care because the rubric is the product spec. Deciding what "good" means for your users is not an engineering task, and until it is written down as evals, your AI feature's quality is whatever nobody happened to complain about this week.

Why can't we just use regular tests?

Because your AI feature is non-deterministic. The same question can produce a differently worded answer every time, and many of those wordings are fine. A traditional test that compares output to an expected string fails constantly and tells you nothing. Worse, the failures that actually hurt you produce no error at all: the confident summary of an empty search result, the polite answer built on the wrong customer's data. I walked through five of these quiet failure patterns in Your AI Agent Has the Same Red Flags as Your Ex; none of them would trip a conventional test suite, and all of them are catchable by a well-aimed eval.

What kinds of evals exist?

Code checks handle everything objective and cheap: response format, schema validity, latency, token counts, presence of a required disclaimer. Run these on everything, always. They are ordinary software.

LLM-as-judge covers the judgment calls. Arize's guide defines it plainly: "an evaluation approach where you use a language model to assess the quality of another model's outputs", where you "define your criteria in plain language and let the judge score outputs at scale". Two shapes matter in practice: single-answer grading, where the judge rates one response against criteria like correctness or groundedness, and pairwise comparison, where the judge picks the better of two responses, useful when you are choosing between prompts or models. The use cases Arize lists read like a product leader's worry list: hallucination detection, RAG relevance, tool-call correctness, summarization quality.

The judge is not magic. It is a prompt, and it is exactly as good as the rubric inside it. If you want to see what a rigorous judge prompt looks like, my free tool Judgekit generates one from a pasted trace, with a built-in stress test.

What do you run evals against?

Two things. First, live production traffic, where evals act as monitoring: every run (or a sample) gets scored, and a dropping score is your early warning. Second, a golden dataset, which Maxim's guide defines as "a curated, versioned collection of prompts, inputs, contexts, and expected outcomes... that becomes the source of truth for measuring quality". Their recommended recipe combines real production samples, user research, and expert-written must-pass scenarios with explicit acceptance criteria. The golden set is what you re-run before every prompt change and model upgrade, which is what turns evals from a dashboard into a regression safety net.

Do not over-build this on day one. A golden set of thirty carefully chosen cases that your team actually maintains beats an aspirational thousand-case benchmark nobody trusts.

What is the product leader's job here?

Three things that engineers cannot do alone. First, define "good" in writing: what must always be true of an answer, what must never happen, and which of those matters most. That document becomes the judge rubrics. Second, arbitrate the gray zone: evals will surface outputs that are defensible but off-brand, and someone with product authority has to rule on them, because every ruling sharpens the rubric. Third, insist that evals come from real failures rather than imagination. The strongest process I know is bottom-up: read production traces, collect what actually went wrong, and write the eval that would have caught each one. The five checks in How to Tell If Your AI Agent Is Actually Working start exactly there.

For what it is worth, this is the shape of my Agent Trust Sprint: failure analysis on your real traffic and 3-5 evaluations set up as your regression safety net, 2 weeks at about 4 working days, EUR 2,400 fixed.

FAQ

How many evals do we need? Fewer than you think. Three to five well-aimed evals, each tied to a failure you have actually seen, cover most products. Grow the set when new failure modes appear, not on a schedule.

Are LLM judges actually reliable? Reliable enough to be useful, if validated. The discipline is to check the judge against human labels on a sample before trusting it at scale, and to re-check when you change the rubric. An unvalidated judge is just a second opinion with confidence issues.

Who should own evals, product or engineering? Both, split cleanly: product owns what the rubrics say, engineering owns that they run everywhere they should. The failure mode is nobody owning either, which is how teams end up with a dashboard of scores no one can explain.

Do we need an eval platform or vendor? Not to start. Your first evals can be a script and a spreadsheet. Platforms earn their keep when you need score history over time, alerting on regressions, and traces linked to scores, which is roughly the point where your AI feature starts mattering to revenue.