Evaluation should reveal whether a system can perform a business task under the conditions it will face, with enough documentation to interpret its mistakes.
Define the decision and input
Provide concrete tasks and the information available when each decision occurs. For classification, describe category boundaries; for extraction, define fields; for retrieval, require answer evidence. Keep later outcomes and reviewer conclusions outside inputs when they would reveal the answer the model is meant to produce.
Build independent evaluation slices
Choose grouping rules for organizations, templates, duplicate documents, related incidents, and time periods. A random split may place nearly identical material on both sides. Reserve the sources or future periods that reflect the generalization question, and report the overlap that remains intentional.
Review reference quality
Use explicit annotation instructions, uncertainty labels, and adjudication records for difficult cases. Inspect evidence behind reference answers and separate unreadable inputs from model errors. Report results by business-relevant slices so aggregate scores do not conceal weak performance on one document family or language.
Start from a bounded pilot
Send Fiund sample tasks, grouping rules, required evidence, and source-review expectations. Potential source fit and permitted scope must be assessed for each project. A pilot can test whether the proposed material supports your evaluation design before you decide what a larger collection should contain.
Include in your inquiry
- Task examples and prediction timing
- Split and duplicate-grouping rules
- Reference evidence and uncertainty policy
- Slice-level acceptance questions