LLM learning

AI Evaluation & Quality Gates

Golden datasets, RAG and agent metrics, offline and online evaluation, regression tests, and CI release gates.

Not started3 min explanation

Visualize, practice, and deep-dive material are optional—use only what helps you learn.

Explanation

A focused 3-minute explanation using the topic's authored material.

Learning goals and prerequisites

After this lesson

  • Build representative golden datasets
  • Measure RAG and agent outcomes and trajectories
  • Create defensible offline and online release gates

Helpful before starting

  • Train-test methodology and basic statistics
  • RAG or agent workflow concepts
  • CI/CD and production monitoring

Start here

AI Evaluation & Quality Gates, in plain language

Golden datasets, RAG and agent metrics, offline and online evaluation, regression tests, and CI release gates. Evaluation turns subjective model behavior into repeatable evidence for iteration, release decisions, monitoring, and rollback.

For a small example, answers vary in correctness, support, and style. Write a criterion-specific rubric, score blindly, cite evidence, and compare human agreement before using an automated judge. This is the mechanism to keep in view as the lesson becomes more technical. Before moving on, identify the input, transformation, output, and one observation that would falsify your conclusion.

Key points

  • Task contracts, rubrics, invariants, fixtures, and labels.
  • Golden, development, private test, and adversarial sets.
  • Deterministic checks, model judges, and human review.

Use it well

When AI Evaluation & Quality Gates helps—and where it breaks

A RAG system answers policy questions. Create fixed cases, measure retrieval recall, citation support, refusal quality, latency, and cost, then inspect failures by slice. A useful result still depends on checking the assumptions and evidence below rather than treating one successful output as proof.

Key points

  • Optimizing one average score. Better approach: Use multi-metric gates and critical slices.
  • Treating model judges as ground truth. Better approach: Calibrate against human-reviewed samples.
  • Changing thresholds to pass a release. Better approach: Predefine policy and document justified revisions.

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