The time split is uncomfortable: on real projects, 60-80% of development time went to error analysis and evalA repeatable test for AI quality — a set of tasks plus scoring — used the way software teams use test suites, because model output is too variable to judge by eyeballing.Full definition →. Evals are part of building, like debugging, not a separate budget line.
A 100% eval pass rate is a bad sign. It means the tests are not stressing the system; something nearer 70% suggests the evals are actually catching real failures.
Error analysis borrows its shape from qualitative research: open coding (free-form notes on traces, tagging the first failure you see), then axial coding (grouping those notes into a failure taxonomy and counting each bucket). The counts decide which evals to build.
Stop reviewing traces at theoretical saturation — the point where new traces stop revealing new failure modes. Rule of thumb: review at least 100, and if roughly 20 in a row add no new category, you are done.
Do not pitch 'evals' to your team, pitch findings. Read 50-100 real conversations, present the top failure modes with how often each happens, and keep a running log of issues caught before users saw them.
Terms in this piece · Glossary
eval — A repeatable test for AI quality — a set of tasks plus scoring — used the way software teams use test suites, because model output is too variable to judge by eyeballing.
LLM — A large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.
LLM-as-judge — Using one model to score another's output against a rubric, so quality can be measured at a scale human grading cannot reach.
Why it matters
A dense, opinionated field guide to LLMA large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.Full definition → evals covering error analysis, LLM-as-judgeUsing one model to score another's output against a rubric, so quality can be measured at a scale human grading cannot reach.Full definition → design, and critique shadowing — the practical workflows for actually improving AI products, not just theory. Useful if you're building AI features and drowning in traces without a systematic way to measure or debug quality.
Key quotes
“In the projects we’ve worked on, we’ve spent 60-80% of our development time on error analysis and evaluation .”
Hamel Husain
“If you’re passing 100% of your evals, you’re likely not challenging your system enough. A 70% pass rate might indicate a more meaningful evaluation that’s actually stress-testing your application.”
“All you get from using these prefab evals is you don’t know what they actually do and in the best case they waste your time and in the worst case they create an illusion of confidence that is unjustified.”
“Use LLMs to scale what you’ve learned, not to avoid looking at data.”
