LEVEL 05 · LESSON 37
Evals
Evals make claims about AI quality testable on the work that matters.
What it is
Evals are systematic tests of how well an AI system performs its intended task. They turn a vague impression of quality into evidence you can compare.
Why it matters
Changes to prompts, retrieval, models, or tools can improve some cases while breaking others. A repeatable evaluation helps detect that tradeoff before users do.
A business example
A support team creates reviewed questions covering ordinary requests, policy exceptions, missing information, and malicious instructions. Every release is checked for supported answers, correct escalation, and unauthorized actions.
How it actually works
Define success in terms of business outcomes, then collect representative cases with expected behavior or scoring criteria. Keep a held-out set separate from examples used for improvement. Use exact checks where possible, human review for judgment, and carefully validated model graders when useful. Inspect failures by category, not only an average score. Offline tests need to be complemented by production monitoring because real traffic changes.
A common misconception
A public benchmark score or a handful of convincing demos cannot establish that your specific workflow is reliable.
ONE SENTENCE TO REMEMBER
Evals make claims about AI quality testable on the work that matters.
CHECK YOUR UNDERSTANDING
A vendor shows a strong public benchmark score and three impressive demos. How do you establish that the model is reliable for your claims-handling workflow?
Show the answer
Answer: C.
Evals make claims about AI quality testable on the work that matters. A public benchmark or a handful of demos cannot establish that your specific workflow is reliable. Inspect failures by category and add production monitoring.
Take it into your next meeting
Which ten difficult cases would you insist on testing before launch?
One more concept, understood.
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