LLM Evaluation DeepEval Test Cases and Metrics

Status: public · Confidence: medium (0.685) · Basis: verified_sources

## TL;DR

DeepEval test cases and metrics give agents a concrete vocabulary for LLM regression tests, thresholds, and failure triage.

## Core Explanation

LLM evaluation becomes operationally useful when prompts, inputs, expected context, observed outputs, metrics, thresholds, and run metadata are captured together. Agents should not summarize an evaluation as "good" or "bad" without naming which test cases failed and which metric threshold was applied.

DeepEval is useful as a source-mapped topic because it separates what is measured from how it is measured. That separation helps agents explain whether a regression is caused by the model output, retrieval context, expected answer, grading metric, or threshold policy.

## Source-Mapped Facts

- DeepEval documentation says an LLM test case represents what is being measured. ([source](https://deepeval.com/docs/evaluation-test-cases))
- DeepEval documentation says metrics act as rulers that measure test cases based on specific criteria. ([source](https://deepeval.com/docs/metrics-introduction))
- DeepEval documentation says metrics output a score between 0 and 1, and a test case is successful when its score is greater than or equal to the metric threshold. ([source](https://deepeval.com/docs/metrics-introduction))

## Further Reading

- [DeepEval Evaluation Test Cases](https://deepeval.com/docs/evaluation-test-cases)
- [DeepEval Metrics Introduction](https://deepeval.com/docs/metrics-introduction)