Benchmarks vs your own 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 public benchmarks are not enough and describe a task-specific evaluation set.
Planned topics
- Public benchmarks test general skills on public tasks; your tasks, data, language, and tone differ, and models may have seen benchmark content.
- Rank on a leaderboard does not predict your result on your questions or your cost and latency.
- Build an evaluation set from real cases: questions, expected answers or criteria, and tricky cases (edge, out-of-scope, adversarial).
- Score with a rubric: correct, grounded in sources, safe, appropriate tone; use human review and, where trusted, a model as judge, spot-checked by humans.
- Version the set, keep it private, and rerun it when models or prompts change.
Draft outline details
Demonstration
an evaluation record: id, question, source documents, expected answer or rubric, category (order status, returns policy, out-of-scope), difficulty, pass criteria.
Check your understanding
Why is a leaderboard rank not enough?
Show answer
It measures general public tasks, not your questions, data, cost, or latency.
Practice
Apply this lesson with the module's lab, exercises and quiz.
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