Public leaderboards are useful for orientation, but they cannot replace task-specific evaluation. Teams need to know whether a model follows output contracts, cites the right evidence, refuses uncertainty, supports human takeover and completes a successful task within budget.
1. The short answer
Define what a good answer means before scoring. A support summary may need to preserve refund conditions, a code change may need passing tests, and an enterprise answer may need evidence for every important claim. Without acceptance criteria, evaluation becomes subjective and hard to reproduce.
2. The context
A task set should include normal, boundary and failure cases, with source inputs, expected outputs, acceptable alternatives and known unanswerable cases. It does not need to be huge at first, but it must reflect real work rather than only easy prompts. Add new production failures so the set stays current.
3. The real trade-offs
One total score hides risk. Split evaluation into factual accuracy, instruction following, citation support, structured output, refusal quality, latency and cost. Rare but high-impact errors need separate release gates instead of being averaged away by many simple wins.
4. How to put it into practice
Keep prompts, context, tools, temperature, output format and retry policy fixed when comparing models. Otherwise you are comparing configurations. Fix the workflow first, swap the model second, and then test routing. Report cost per successful task and the amount of human correction required.
5. Common failure modes
Teams often test only first-turn answers, ignore long context, hide human edits or keep an old baseline after a model upgrade. Treat evaluation data like code tests: run it before release, keep failure examples and investigate regressions before users discover them.
6. FDE perspective
Leaderboards are useful for narrowing options, not for making the final purchase decision. A durable model catalog stores task performance, version, cost, latency, failure cases and boundaries so future decisions remain tied to business outcomes.
A practical checklist before shipping or buying
- Define acceptance criteria before selecting test cases.
- Keep non-model variables fixed for fair comparison.
- Set separate gates for high-impact errors.
- Track human correction and cost per successful task.
What to do next
Start with a 20–50 case baseline for one real workflow before expanding a model catalog.
This article is based on public documentation and research from Google ML evaluation guidance. The FDE perspective is editorial analysis, not a quotation from the source.
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