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Model Evaluation

Model evaluation measures whether an AI system behaves well enough for its intended use.

What Is Model Evaluation​

Evaluation compares model outputs against expectations, examples, rules, metrics, or human review.

It is especially important for LLM apps because output quality can vary by prompt, context, and model version.

How To Use Model Evaluation​

Create test cases, define quality criteria, run the model, inspect failures, and track changes over time.

Basic Example​

{
  "input": "Explain HTTP to a beginner",
  "expected": "mentions request, response, client, and server",
  "score": 0.92
}

Common Concepts​

  • Test sets represent real tasks.
  • Metrics measure specific behavior.
  • Human review catches nuance.
  • Regression checks prevent quality drops.

What To Learn Next​

Learn golden datasets, rubric-based grading, hallucination checks, retrieval evaluation, and production monitoring.