LLM systems change behaviour when you change a prompt, a model version or a retrieval setting. Without evals, nobody knows whether the change helped.
The problem with vibes
Most teams judge AI quality by trying a few prompts and reading the answers. That catches the obvious and misses the rest. Then a model upgrade ships, answers get subtly worse, and the first signal is a support ticket two weeks later. The feedback loop is your users.
What an eval suite is
A set of real inputs with expected outcomes, graders that score the output automatically, and thresholds you agree on. Exact match where possible, rules where they fit, model-graded rubrics where you must, with human spot checks on the graders. It runs on every change, and a drop below the threshold blocks the merge.
The feedback loop should not be your users.
How we build it
We map the failure modes that actually hurt your users, ranked by impact, from a workshop and sample traffic. We build the datasets and graders with your team, wire them into CI, and review the scores monthly: what regressed, what improved, which new failure modes to add. The suite grows with the system.