Building an LLM eval stack: what actually matters

Teams shipping LLM features eventually hit the same wall: manual spot-checking catches obvious failures but misses regressions, and it doesn't scale past a handful of engineers eyeballing outputs. The fix isn't necessarily an elaborate evaluation platform — it's a small set of practices applied consistently.

Start with a golden dataset

The highest-leverage first step is a golden dataset: a curated set of real (or realistic) inputs with known-good outputs, pulled from actual production traffic or support tickets rather than invented test cases. It doesn't need to be large — fifty to a few hundred well-chosen examples that cover your actual edge cases beat a thousand generic ones. This becomes the regression suite that runs on every prompt or model change.

Be honest about LLM-as-judge

Using a second LLM to grade the first model's outputs is common and useful, but it has a real failure mode: judge models tend to have their own biases (favoring longer or more confident-sounding answers, for instance) that don't track what you actually care about. Calibrate the judge against human-labeled examples periodically rather than trusting its scores blindly, and use it as a triage signal to find what needs human review, not as the final word.

Where teams overinvest

It's common to see teams build extensive eval dashboards and custom metrics before they have more than a few dozen labeled examples to evaluate against. Off-the-shelf tooling is worth adopting once the labeling and dataset curation work is solid — before that point, it mostly adds process overhead without improving signal.