Your most valuable AI asset isn’t the model.
Models change. Prompts change. A thoughtful evaluation set preserves what your business knows about a good answer, and turns that knowledge into a better product.
Six impressive examples can win a room. They cannot tell you whether an AI product is ready for the people who will depend on it.
The useful question is what happens beyond those examples. Which tasks does the system handle? Where does it struggle? Did the latest change help, or just move the problem?
A shared evaluation set gives those questions somewhere concrete to land.
Put the experts in the loop
Start with the people doing the work today. They know which cases are ordinary, which are unusual, and which seemingly small mistakes matter.
Ask for real examples of both straightforward and difficult work. Engineers should help turn them into repeatable checks, while domain experts define what a useful result looks like.
Make contributing easy. A spreadsheet that people keep updating is more valuable than an elaborate tool they avoid.
Give each case a reason to exist
Three fields make a strong starting point:
- Input: The real request or document, including its awkward details.
- Expected outcome: The facts, actions, citations, or refusals a good response should contain.
- Why it matters: The behaviour this case is intended to test.
That final explanation gives future reviewers a way to judge the significance of a failure. Without it, an evaluation set can become a collection of answers nobody remembers choosing.
Use more than one kind of judgment
Deterministic checks are useful for concrete requirements: a figure, a source, an output format, or an expected refusal.
Model-based judges can help with open-ended answers when they follow a clear rubric. Check their decisions against human-labelled examples so you understand their limits.
Keep human review in the process. Sampling real outputs can reveal changes that neither assertions nor automated judges were designed to catch.
Turn failures into direction
An overall score is a useful signal. The failing cases are often the more valuable product insight.
A system that struggles with older records or multi-party claims needs a different next step from one that simply misses formatting requirements. Specific failures give the team something to improve.
Over time, the evaluation set becomes a record of what your business considers good. You can use it to compare prompts, assess a new model, or change providers with evidence. That knowledge deserves to remain yours.