AI
Evaluate a RAG system
RAG evaluation checks whether the system retrieved the right passages and whether the answer is faithful to them. A high similarity score is not that check.
For people who already have a RAG prototype and need evidence about when it fails.
Start here
Skim the RAGAS docs for the metric names, then write five questions whose correct source you already know.
Recommended resources
Start here · IntermediateRAGAS documentation
Defines faithfulness, answer relevancy, and context precision in one evaluation library.
docs.ragas.ioPaper · AdvancedRAGAS: Automated Evaluation of Retrieval Augmented Generation
The paper behind the metrics, so the score names have a definition.
arxiv.orgReference · IntermediateRAGAS repository
The implementation and examples to run against a dataset you control.
github.comContext · AdvancedOriginal RAG paper
The system you are measuring. Evaluation is easier once the pipeline stages are distinct.
arxiv.orgRelated topics
Learn retrieval-augmented generation · Evaluate language model systems · Choose and use a vector database