What is Retrieval-Augmented Generation (RAG)?

RAG solves the fundamental limitation of Large Language Models: they only know what they were trained on. Your company's SOPs, pricing sheets, customer histories, and technical documentation don't exist in GPT-4's training data. RAG bridges this gap through a three-stage pipeline. Stage 1 (Ingestion): Documents are chunked into semantically meaningful segments (typically 500-1500 tokens), converted into vector embeddings using models like text-embedding-3-large, and stored in a vector database (pgvector, Pinecone, or Weaviate). Stage 2 (Retrieval): When a user asks a question, the query is embedded into the same vector space and a similarity search returns the top-K most relevant document chunks. Stage 3 (Generation): The retrieved chunks are injected into the LLM's context window alongside the original question, grounding the model's response in your actual data. Advanced RAG implementations add re-ranking (using cross-encoder models to re-score retrieved chunks for relevance), hybrid search (combining vector similarity with keyword BM25 matching), and query decomposition (breaking complex questions into sub-queries). The result is an AI assistant that answers questions with the accuracy of your internal documentation and the fluency of a large language model.