Golden datasets, RAG and agent metrics, offline and online evaluation, regression tests, and CI release gates.
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.
A RAG system answers policy questions. Create fixed cases, measure retrieval recall, citation support, refusal quality, latency, and cost, then inspect failures by slice.
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Predict how one control changes the AI Evaluation & Quality Gates result.
The starter deliberately follows a tempting but flawed assumption: Optimizing one average score
Check: The browser checks pass and you can name the failure mechanism before opening the explanation.
Failing starter lab
A release gate passes only when successes / trials meet a threshold.
Run the incomplete starter before opening hints. Use the first wrong output or failed check to form a repair hypothesis.
Explanations after each answer · 80% to pass · AI Evaluation & Quality Gates
Chapter 1 of 4 · Core
An evaluation starts by defining what success means for a concrete task. Specify allowed tools, required artifacts, forbidden side effects, latency and cost limits, and scoring rules. A vague instruction such as “be helpful” cannot support a release gate.
A golden dataset contains representative inputs, expected outcomes, and metadata for slicing. For agents, it may also contain acceptable tool sequences, required checkpoints, environment fixtures, and invariant checks. Include normal cases, ambiguous requests, tool failures, malicious inputs, and recovery paths.
Version examples and labels. Track who reviewed them, why an answer is acceptable, and which production failure motivated each case. Hold out a private test split to reduce prompt overfitting.
Two free references selected for AI Evaluation & Quality Gates.
Primary sources and free technical reading for AI Evaluation & Quality Gates.
Open-source evaluation workflows and metrics for retrieval-augmented generation.
Open-source unit-style evaluation framework for LLM and RAG applications.
A production-readiness rubric connecting tests, monitoring, and operational maturity.
A public framework for governing, mapping, measuring, and managing AI risk.