What is a RAG System
RAG (Retrieval-Augmented Generation) is an architectural pattern where an LLM (GPT-4, Claude) first retrieves relevant documents from your knowledge base (via vector search), then generates an answer based on the retrieved context. This delivers accurate answers from internal company data not present in LLM training sets.
Definition
RAG solves a fundamental LLM problem — they don't know your company, policies, documentation. Without RAG, models "hallucinate" — invent answers. With RAG, they respond strictly based on provided data. Applications: internal support chatbots, document search, legal assistants, new employee training.
How It Works
RAG architecture: 1) Documents (PDF, Notion, Confluence) are split into chunks. 2) Each chunk passes through embedding model (text-embedding-3) and is stored in vector DB (Pinecone, Qdrant, pgvector). 3) On user query — search for similar chunks by semantic proximity. 4) Retrieved context + question fed to LLM (GPT-4o, Claude 3.5). 5) Model generates response strictly based on context.
When to Use
RAG fits when: you need to answer from internal documents (legal policies, technical manuals, HR policies), documentation is large (can't fit in prompt), source citation is required (compliance), or multilingual support is needed.
When NOT to Use
RAG does not fit when: tasks require complex mathematical computations (need function calling and tools), creative generative content without fact-grounding is required, or data changes every second (RAG assumes periodic reindexing).
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