How does RAG make AI models smarter without retraining them?

The answer

Retrieval-Augmented Generation (RAG), first described by Facebook AI Research in 2020, combines a language model with an external knowledge retrieval system. When a user asks a question, the system first searches a vector database of documents to find relevant context, then feeds that context to the LLM alongside the query. This dramatically reduces hallucinations, keeps answers grounded in up-to-date facts, and lets organizations use AI over their private data without expensive fine-tuning. By 2024-2025, RAG became the standard enterprise architecture for deploying LLMs, with advanced variants like GraphRAG and corrective RAG pushing accuracy even further.