[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"glossary-eval-harness::en":3,"gloss-cluster-eval-harness::en":26,"gloss-next-eval-harness::en":9},{"slug":4,"category":5,"name":6,"definition":7,"meta_desc":8,"faq":9,"schema_markup":9,"related":10},"eval-harness","mlops","Eval Harness","An eval harness is an automated test suite for model outputs — the machine-learning equivalent of unit tests. It pairs a fixed dataset of inputs with scoring logic: exact-match checks, regex or schema validation, rubric grading, or an LLM-as-judge that rates each response. Run the harness on every prompt tweak, model upgrade, or fine-tune, and you get a comparable score instead of a gut feeling. For SaaS builders shipping AI features this is the difference between confident iteration and praying nothing broke. Without a harness you eyeball five examples, ship, and discover the regression from an angry customer. With one, a prompt change that quietly worsens 12% of cases shows up as a red number in CI. Tools include promptfoo, OpenAI Evals, LangSmith, and Braintrust. Practical note: start small with twenty to fifty real cases you actually care about, mix cheap deterministic checks with a few judge-based scores, and grow the set every time a bug escapes to production.","An eval harness is a unit-test suite for model outputs: a fixed dataset plus scoring — exact match, schema checks, rubrics, or an LLM judge — run on every change.",null,[11,14,17,20,23],{"slug":12,"name":13},"ci-cd","Continuous Integration \u002F Continuous Deployment (CI\u002FCD)",{"slug":15,"name":16},"golden-dataset","Golden Dataset",{"slug":18,"name":19},"llm-as-judge","LLM-as-Judge",{"slug":21,"name":22},"llm-benchmark","LLM Benchmark",{"slug":24,"name":25},"prompt-testing","Prompt Testing",[27,31,35,38,41,45,48,51,54,57,60,63],{"slug":28,"category":5,"name":29,"updated_at":30},"annotation-guidelines","Annotation Guidelines","2026-08-24T03:30:02+00:00",{"slug":32,"category":5,"name":33,"updated_at":34},"baseline-model","Baseline Model","2026-08-24T02:46:38+00:00",{"slug":36,"category":5,"name":37,"updated_at":34},"batch-inference","Batch Inference",{"slug":39,"category":5,"name":40,"updated_at":34},"canary-prompt","Canary Prompt",{"slug":42,"category":5,"name":43,"updated_at":44},"champion-challenger","Champion-Challenger (A\u002FB Model Testing)","2026-08-24T02:46:37+00:00",{"slug":46,"category":5,"name":47,"updated_at":34},"class-imbalance","Class Imbalance",{"slug":49,"category":5,"name":50,"updated_at":34},"continuous-batching","Continuous Batching",{"slug":52,"category":5,"name":53,"updated_at":34},"cross-validation","Cross-Validation",{"slug":55,"category":5,"name":56,"updated_at":34},"data-labeling","Data Labeling",{"slug":58,"category":5,"name":59,"updated_at":44},"drift-detection","Drift Detection",{"slug":61,"category":5,"name":62,"updated_at":44},"experiment-tracking","Experiment Tracking",{"slug":64,"category":5,"name":65,"updated_at":34},"explainability","Explainability"]