How would I know this tool works for my actual task?
Evaluation
Evaluation compares a system’s behavior with explicit criteria on a defined set of tasks. A single impressive example gives little information about failure rates.
An everyday example
Before using a summary workflow, try ordinary, ambiguous and incorrect-input cases. Check accuracy, missing facts, time and recovery effort.
Original illustration, not an observed participant case.
Where the idea stops
A benchmark result does not automatically transfer to your documents, users or stakes. A small practice exercise is not a certification.
Something to try
Write the acceptance rule before running a tool. Include a case it should refuse or escalate, and a valid case it should accept.
Practise on a fictional case →Go deeper: the builder’s view
For builders: keep dated cases, model and prompt versions, outcomes and costs. Compare with an AI-off baseline. Re-test when real inputs differ from the evaluation set: this is distribution shift.
Source and interpretation
This is a plain-language interpretation with an applied example; it is not a quotation or an endorsement from the source.
Liang et al. — Holistic Evaluation of Language Models (2022) ↗Catalog v0.1 · checked 2026-10-29