Deepeval
Open-source LLM eval framework
2 case studies
Data Dev Framework Open-source LLM eval framework
DeepEval is used to unit-test LLM applications with metrics like faithfulness, answer relevancy, hallucination detection, contextual recall, and toxicity. It integrates with pytest so you can add LLM evaluation to your existing CI/CD pipeline and catch regressions before deployment.
Install with `pip install deepeval` and set your OPENAI_API_KEY (used for metric computation). Write test files using `assert_test()` with test cases and metrics like `FaithfulnessMetric()` or `AnswerRelevancyMetric()`. Run with `deepeval test run test_file.py` or standard pytest to get pass/fail results with explanations.
$ pip install deepeval` and set your OPENAI_API_KEY (used for metric computation Case studies
200k-user AI coding tool
A popular AI coding tool was shipping regressions to 200k users because their LLM quality checks were ad-hoc and manual. User-reported quality issues were their top support category.
Built an 800-case Deepeval test suite covering correctness, coherence, toxicity, PII leakage, and brand voice. Integrated into CI/CD with automatic blocking on quality regression. Added weekly eval reports to engineering standup.
Critical bugs caught before release increased 4x. User-reported quality issues per release dropped 67%. The engineering team now ships with confidence — one eval run replaced three days of manual QA.
AI startup, pre-Series A
A pre-Series A AI startup needed to demonstrate production-ready quality to investors. Their informal evaluation process hadn't caught two critical safety and accuracy failures that would have been discovered in diligence.
Built a 500-case Deepeval harness covering factuality (measured vs ground truth), safety (toxic outputs, jailbreak attempts), calibration (confidence vs accuracy), and instruction following. Ran against all 3 candidate models.
Caught 2 critical failures: a safety bypass in Model A and systematic hallucination on out-of-distribution inputs in Model B. Fixed before investor demo. Eval suite adopted as the company's permanent quality gate. Series A closed at $8M.
Used Deepeval professionally?
Add your case study and get discovered by clients.
Submit a case studySubmit a brief and we'll match you with vetted specialists who have proven Deepeval experience.