Quality & operations
AI Evaluation: How to Measure AI Applications
Why "it looks good to me" is not an evaluation strategy, and what to build instead.
Most AI projects start with an evaluation process of exactly one test: does it look good when I try it myself? That approach breaks down almost immediately once real users, edge cases, and changes over time enter the picture.
A real evaluation process replaces impressions with a repeatable, measurable process you can run every time something changes.
Key takeaways
Start with a test set, not a vibe check
Collect 30-100 real or realistic examples that represent the range of inputs your system will actually see, including tricky edge cases. For each, define what a correct or acceptable answer looks like.
This test set becomes the foundation everything else is built on — without it, "better" and "worse" are just opinions.
Combine automatic scoring with human review
Some qualities can be scored automatically — did the output include required fields, does it match an expected format, is a specific fact present. Others, like tone or nuanced correctness, still need human review, at least on a sample.
A practical evaluation process usually blends both: automatic checks catch obvious regressions quickly, and periodic human review catches subtler quality issues.
Make evaluation part of your workflow, not a one-time event
Run your evaluation set every time you change a prompt, swap a model, or adjust retrieval — not just before the initial launch. Model providers update their models, and behavior can shift underneath you without any code change on your side.
Track scores over time so a quiet regression shows up as a trend, not a surprise complaint from a user weeks later.
Put this into practice