Why LLM features need evaluations
On This Lesson 4 topics
This lesson is planned. The outline below lists the topics it will cover. The full lesson has not been published yet.
In this lesson
explain why normal tests are not enough.
Planned topics
- LLM output is variable and open-ended; an exact-match assertion rarely fits.
- A prompt, model, or retrieval change can silently make answers worse; nobody notices until customers complain.
- An evaluation (eval) is a repeatable test set with scoring that turns "seems fine" into numbers.
- Evals gate releases, like unit tests and contract tests.
Draft outline details
Demonstration
two runs of the order-support assistant on the same questions: groundedness 92% versus 71% after a prompt tweak.
Check your understanding
What problem do evals solve?
Show answer
Silent quality drops after prompt, model, or retrieval changes.
Practice
Apply this lesson with the module's lab, exercises and quiz.
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