The principle
- Each evaluation dataset needs a set of evaluators that matches its purpose
4:18
Example 1 — "happy path" dataset (questions + reference answers)
- Compare new outputs to ground truth with: semantic similarity, exact matches, or LLM-as-a-judge
4:18 - Add simpler checks: the link is present where needed, the response isn't too long
4:34
Example 2 — edge cases (e.g. questions about competitors, expecting a polite decline)
- Evaluate with an LLM judge
4:34 - Or semantic similarity against a polite-denial template
4:34 - Or a simple regular expression when a specific canned response is expected
4:34
Wrap-up
- The video is an overview of LLM evaluation methods; the next one covers LLM-as-a-judge, one of the most popular approaches
5:00