[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"glossary-error-analysis::en":3,"gloss-cluster-error-analysis::en":23,"gloss-next-error-analysis::en":9},{"slug":4,"category":5,"name":6,"definition":7,"meta_desc":8,"faq":9,"schema_markup":9,"related":10},"error-analysis","mlops","Error Analysis","Error analysis is the practice of reading individual failures and grouping them, rather than reading the score. The score answers how much; error analysis answers what, and only the second one tells you what to build next. The method is unglamorous. Pull the cases the system got wrong, take a few dozen of them, read each one, and write a short phrase describing what actually went wrong — not a grade but a cause. Then sort the phrases into groups and count. What almost always emerges is that a small number of distinct problems account for most of the failures, and that they are not the problems the team had been discussing. The retrieval step returned nothing useful for a whole class of question. The system answered a different question from the one asked. The output was correct but in a format the downstream step rejected. Those are four different pieces of work with four different owners, and the aggregate score contained none of that information. Doing it well requires a few disciplines. Sample rather than cherry-pick, since the memorable failures are rarely the common ones. Read the input as well as the output, because a surprising share of errors turn out to be cases that were unanswerable as posed. Keep the notes in a file rather than in a meeting, so the second round can be compared with the first. And re-run the analysis after a fix rather than trusting the score to confirm it, since the score can improve while the group you targeted is untouched and a different one has quietly shrunk. The output of a good session is a ranked list of causes, each with a count and a name, which is the raw material for both a fix list and a taxonomy. It is also the cheapest way to discover that a system is failing silently. Aggregate scores are computed over cases that produced an output; the ones that timed out, returned nothing, or were dropped before scoring are frequently missing from the denominator entirely, and reading a sample of raw runs rather than a results table is usually how somebody notices.","Error analysis reads failures one by one and groups them. Why the score says how much and only this says what, plus the disciplines that make it work.",null,[11,14,17,20],{"slug":12,"name":13},"eval-harness","Eval Harness",{"slug":15,"name":16},"offline-evaluation","Offline Evaluation",{"slug":18,"name":19},"precision-and-recall","Precision and Recall",{"slug":21,"name":22},"silent-failure","Silent Failure",[24,28,31,35,38,41,44,47,50,53,56,59],{"slug":25,"category":5,"name":26,"updated_at":27},"acceptance-threshold","Acceptance Threshold","2026-09-10T03:30:02+00:00",{"slug":29,"category":5,"name":30,"updated_at":27},"adjudication","Adjudication",{"slug":32,"category":5,"name":33,"updated_at":34},"annotation-guidelines","Annotation Guidelines","2026-09-08T21:32:36+00:00",{"slug":36,"category":5,"name":37,"updated_at":34},"baseline-model","Baseline Model",{"slug":39,"category":5,"name":40,"updated_at":34},"batch-inference","Batch Inference",{"slug":42,"category":5,"name":43,"updated_at":27},"blind-evaluation","Blind Evaluation",{"slug":45,"category":5,"name":46,"updated_at":34},"canary-prompt","Canary Prompt",{"slug":48,"category":5,"name":49,"updated_at":27},"ceiling-effect","Ceiling Effect",{"slug":51,"category":5,"name":52,"updated_at":34},"champion-challenger","Champion-Challenger (A\u002FB Model Testing)",{"slug":54,"category":5,"name":55,"updated_at":34},"class-imbalance","Class Imbalance",{"slug":57,"category":5,"name":58,"updated_at":34},"continuous-batching","Continuous Batching",{"slug":60,"category":5,"name":61,"updated_at":27},"coverage-gap","Coverage Gap"]