---
id: llm-evaluation-deepeval-test-cases-and-metrics
title: 'LLM Evaluation DeepEval Test Cases and Metrics'
schema_type: TechArticle
category: ai
language: en
confidence: medium
last_verified: '2026-06-03'
created_date: '2026-06-03'
generation_method: ai_structured
derived_from_human_seed: true
conflict_of_interest: none_declared
is_live_document: false
data_period: static
atomic_facts:
  - id: fact-ai-llm-evaluation-deepeval-test-cases-and-metrics-1
    statement: >-
      DeepEval documentation says an LLM test case represents what is being
      measured.
    source_title: DeepEval Evaluation Test Cases
    source_url: https://deepeval.com/docs/evaluation-test-cases
    confidence: medium
  - id: fact-ai-llm-evaluation-deepeval-test-cases-and-metrics-2
    statement: >-
      DeepEval documentation says metrics act as rulers that measure test cases
      based on specific criteria.
    source_title: DeepEval Metrics Introduction
    source_url: https://deepeval.com/docs/metrics-introduction
    confidence: medium
  - id: fact-ai-llm-evaluation-deepeval-test-cases-and-metrics-3
    statement: >-
      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_title: DeepEval Metrics Introduction
    source_url: https://deepeval.com/docs/metrics-introduction
    confidence: medium
completeness: 0.82
known_gaps:
  - DeepEval results depend on metric choice, threshold, model version, judge prompt, dataset representativeness, stochastic settings, and whether failures are reviewed by humans.
disputed_statements: []
primary_sources:
  - title: DeepEval Evaluation Test Cases
    type: documentation
    year: 2026
    url: https://deepeval.com/docs/evaluation-test-cases
    institution: DeepEval
  - title: DeepEval Metrics Introduction
    type: documentation
    year: 2026
    url: https://deepeval.com/docs/metrics-introduction
    institution: DeepEval
secondary_sources: []
updated: '2026-06-03'
ai_models:
  - gpt-5-codex
---

## 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)