DevOps & Tooling / Knowledge library
LLM Evals
repeatable tests for model behavior and application quality
Why learn LLM Evals?
Useful for measuring whether AI systems improve, regress or satisfy task-specific quality requirements.
Where it came from
LLM evals define criteria, datasets and graders to test model or application behavior across runs and configurations.
Build your understanding
Work through these exercises in order. Try the task before opening a reference.
- Summarize the official positioning for LLM Evals in one paragraph.
- Identify the buyer, developer, or user workflow LLM Evals is built for.
- Compare LLM Evals with a neighboring catalog topic and name the boundary.
- List two implementation risks or adoption questions for LLM Evals.
- Design a five-question evaluation that would test whether someone understands LLM Evals.
Documentation and sources
- OpenAI Evals API reference (platform.openai.com)
Catalog research notes
- Research snapshot: September 20, 2026. This entry summarizes the linked public documentation and should be refreshed before making version-specific claims.
Connect the ideas
Built on
Enables
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