Evaluation.

Evaluation connects an expectation with evidence. This reading path explores how to define a task, keep representative examples, review failure cases, and record the configuration behind a result. It brings together AI asset organization, language model selection, and prompt versioning.

Begin with the decision you want the evidence to support. You might be comparing two deployment options, checking a changed prompt, or deciding whether a workflow is ready for a wider group of users. The guides suggest practical records and examples instead of a universal score. Keep the examples relevant to the work, document what the checks miss, and review results before treating a change as an improvement.