Legal RAG Bench
Legal RAG benchmark revealing embedding quality > LLM choice by 19-point margin.

Predicts RAG benchmark transfer failure using vocabulary specificity—no embeddings needed.
ML engineers building RAG systems
Ragas · Arize Phoenix · TruLens
Legal RAG benchmark revealing embedding quality > LLM choice by 19-point margin.
Modular RAG with MCP integration, but Langchain and LlamaIndex already dominate.
Swaps fragile vector embeddings for deterministic BM25 search to fix RAG reliability.
Replaces vector databases with BM25 keyword search for transparent retrieval.
BM25 search beats vector drift when you need to know exactly why a doc was retrieved.
First public NRC regulatory embeddings dataset—37K chunks ready for ChromaDB and Pinecone.