RAG improves LLM reliability by grounding responses in external data. We benchmark the RAG stack across embedding models, rerankers, vector databases, frameworks and evaluation tools on retrieval accuracy, latency and quality.
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Because most products have few duplicate reviews in the candidate set, Recall@10 and Hit@10 are nearly identical in this benchmark.
We benchmarked five retrieval-augmented generation (RAG) evaluation tools on 1,000+ adversarial questions, each paired with five passages.
We benchmarked 25 embedding models on 200 retrieval questions across finance, legal contracts, technical support and medical literature.
We benchmarked seven open-source, self-hosted vector databases as the retrieval layer of a RAG pipeline, each run one at a time on identical bge-m3 embeddings and real medical and technical queries, so the database index was the sole variable.