GUIDE · AIunipedia Community
An evaluation set should represent the work your application is expected to perform. A collection of easy prompts selected after seeing good answers is not strong evidence of reliability.
Choose cases around meaningful failure costs, using appropriate error metrics where they fit. The AI tool scorecard shows how to include editing effort and operational constraints in a comparison.
Start with a task taxonomy
For a fictional policy assistant, define categories such as direct lookup, missing information, ambiguous wording, conflicting versions and out-of-scope requests. Add permission-sensitive cases where relevant.
For each case, record the input, permitted reference material, expected behaviour and a scoring rubric. Some tasks need an exact answer; others need evidence of several required elements or a justified escalation.
Keep development and evaluation distinct
Use development examples to improve your prompt or retrieval configuration. Preserve a separate held-out set for a final comparison. Inspect cases by category so an overall average does not hide a serious weakness.
If using a model as a judge, test the judging rubric against human-reviewed examples. A judge model can be inconsistent or reward a persuasive answer that lacks support. Keep disagreements and describe how they were resolved.
Share useful results
Report the configuration, sample size, scoring process and representative failures. State whether examples are synthetic, collected from real use with permission or adapted from a public dataset.
Discussion: Which failure category would be most damaging if your evaluation set omitted it?
Reference: Hugging Face RAG evaluation cookbook.
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