TruLens

Automatic RAG Evaluation System - a framework for assessing RAG system quality through automated evaluation of retrieval relevance and generation accuracy without human labels.

Retrieval Augmented Generation Assessment framework for reference-free evaluation of RAG pipelines. RAGAS provides automated metrics for retrieval quality, context relevance, and generation faithfulness.

An AI observability and evaluation platform that helps monitor and evaluate LLM outputs, RAG pipelines, and data quality, with tools for detecting hallucinations and measuring retrieval quality.

Research paper on operating and managing Retrieval-Augmented Generation (RAG) pipelines at scale, covering production infrastructure patterns, monitoring, microservices decomposition, and multi-model architecture for enterprise embedding systems.

Using language models to automatically evaluate RAG system outputs, retrieval quality, and answer correctness. LLM-as-judge provides scalable, consistent evaluation of aspects like faithfulness, relevance, and coherence that are difficult to measure with traditional metrics, enabling rapid iteration on RAG systems.

Comprehensive overview of frameworks and tools for evaluating RAG systems including RAGAS, TruLens, LangSmith, and ARES with metrics for retrieval quality, generation accuracy, and end-to-end performance.