Retrieval-Augmented Generation

Retrieval-Augmented Generation (RAG) is the complete term for the architectural pattern commonly abbreviated as RAG, which combines information retrieval from external knowledge sources with language model text generation to produce responses grounded in verifiable source material. The pattern was introduced by Lewis et al. in 2020 and has become the dominant approach for building AI applications that need to access specific, current, or proprietary knowledge.

The RAG architecture addresses fundamental limitations of standalone language models. LLMs have a knowledge cutoff date beyond which they have no information. They cannot access proprietary or internal organizational data. They may hallucinate facts that sound plausible but are incorrect. And they cannot provide citations or sources for their claims. RAG solves these problems by retrieving relevant documents before generation, giving the model access to current, authoritative information that it can reference and cite.

RAG system design involves critical decisions at each pipeline stage. Document processing choices include file format handling, text extraction quality, and metadata preservation. Chunking strategies balance between semantic completeness and retrieval granularity. Embedding model selection impacts semantic matching quality across languages and domains. Vector database configuration affects retrieval speed and scalability. Retrieval strategy, whether dense, sparse, or hybrid, with or without reranking, determines the quality of retrieved context. Prompt design controls how retrieved context is presented to the generation model.

Advanced RAG architectures extend the basic retrieve-then-generate pattern. Multi-step RAG decomposes complex queries into sub-queries, each retrieving different information. Corrective RAG evaluates retrieval quality and falls back to alternative strategies when initial results are insufficient. Self-RAG trains models to decide when retrieval is needed and to evaluate retrieval relevance. GraphRAG combines vector retrieval with knowledge graph traversal for relationship-rich queries. These advances continue to improve RAG reliability and capability for enterprise applications.