RAG (Retrieval-Augmented Generation)

Definition

An advanced AI architecture that enhances large language model (LLM) responses by first retrieving relevant information from a verified knowledge base before generating answers. RAG grounds AI responses in factual, verifiable content—dramatically reducing hallucinations and improving accuracy through source-backed intelligence.

Why It Matters

RAG reduces AI hallucinations by 85-95% compared to traditional LLMs, provides current information without model retraining, offers source transparency with explicit citations, and leverages organization-specific knowledge. Critical for legal applications where accuracy and verifiability are non-negotiable, achieving 95%+ accuracy vs 60-70% for generic LLMs.

Related Use Cases

• Obligation Tracking

Related Modules