and confidence‑based evaluators

DeepEval quantifies LLM performance using G-Eval metrics and integrated confidence scores to ensure judge reliability.

DeepEval (developed by Confident AI) provides a testing framework that treats LLM evaluation like traditional unit testing. It uses G-Eval to score outputs on a 0 to 1 scale while generating a corresponding confidence score for each result: this allows developers to identify and discard low-certainty evaluations. The tool integrates with Pytest and supports metrics like faithfulness, answer relevancy, and hallucination detection. By leveraging logprobs and reasoning steps, DeepEval offers a transparent look into why a model passed or failed (providing the specific reasoning behind every score).

https://www.confident-ai.com