LLM Development: Define Your Evaluation Criteria Early
An LLM demonstration can appear convincing long before it is dependable in a customer workflow. Evaluation criteria make the product team agree on what “useful” means before a model is connected to real data or decisions.
Turn the Use Case Into Test Cases
Collect representative inputs, including ordinary, incomplete, ambiguous, and sensitive cases. Define what a useful response must include, what it must never do, and when it should decline or escalate. Keep the set small enough to review regularly.
Measure More Than Accuracy
Accuracy does not capture whether an answer uses approved evidence, follows the intended format, or enables the next action. Combine quality checks with completion, correction, and escalation measures. AI product metrics beyond accuracy gives a practical framework.
| Criterion | Example question |
|---|---|
| Grounding | Did it use the approved source? |
| Usefulness | Can the user act on it? |
| Safety | Did it escalate an uncertain case? |
Review Criteria as the Product Changes
Update the evaluation set when user roles, data sources, or actions change. A stable model can still create a new product risk when its surrounding workflow expands. For production planning, see how testing must change from prototype to production.
Test LLM Value Before You Scale It
MVPHUB can help you define product-focused LLM evaluations, failure paths, and a build plan grounded in evidence.
Book a free consultation with MVPHUBFrequently Asked Questions
What should an LLM evaluation measure?
Measure the product outcome as well as output quality: whether the response is grounded, complete enough, safe to use, and helps a user complete the intended task.
