Jev AI use cases
Evaluation & quality
Check generated answers and agent outcomes against the evidence and rules you supply.
Evaluation & quality
Check generated answers and agent outcomes against the evidence and rules you supply.
LLM as a Judge
Evaluate an answer for source support, relevance and quality using your own rubric.
- Answer quality
- Groundedness
- Rubrics
AI Agent Evaluation
Check completion claims against tool results, task requirements and allowed actions.
- Task completion
- Tool evidence
- Rule compliance
About evaluation & quality
Evaluation turns “did the AI do the right thing?” into questions you can answer the same way every time. Jev fits the evaluator role: it reads the material you supply and returns typed verdicts, probabilities and scores instead of a written critique, so results can be counted, compared and thresholded in code.
Pick the page by what you are checking. An answer is judged against its reference; an agent run is judged against its task, scope and the evidence of what actually happened. Both keep separate criteria as separate questions, so one strong signal cannot hide a failure in another.
Which page fits your task
- LLM as a JudgeYou have a generated answer and the source or reference it should rely on, and want groundedness, relevance and completeness checks.
- AI Agent EvaluationYou have an agent trace with actions and tool results, and need to know whether the task was completed within the allowed scope.
Common mistakes
- Collapsing several criteria into one overall score, which hides which part failed.
- Treating a confident verdict as proof. Validate against human-labeled examples first.
- Letting the judge rely on its own knowledge when the check is about the supplied source.
Evaluation & quality FAQ
Is Jev an evaluation framework?
No. Jev is the evaluator in the loop: it answers typed questions about the material you send. Collecting traces, storing datasets and computing aggregate metrics stay in your own pipeline or evaluation tool.
Can I run an evaluation over a whole dataset?
Yes. Save your judge, then run it in Batch on a CSV or JSONL file of up to 1,000 rows, or call the API from your pipeline for larger sets.
Further reading · reviewed 2026-09-23
- Jev and typed decisionsTypeSafe documentation · docs.typesafe.ai/introduction
- Combining independent checks in codeTypeSafe documentation · docs.typesafe.ai/patterns/composite-scoring
One decision model. Your own rules.
Use a Noul for a yes/no probability, a Choice for a fixed set of outcomes, or a Score for an ordered rubric. Ask focused questions about the same input and combine the results in code.