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RAG System

Retrieval-Augmented Generation - Ground AI responses in your own data

retrievalmedium complexity
Formula
PrPrompts+EmEmbeddings+VxVector DB+LgLLM

Overview

RAG is the most widely-deployed AI architecture. It combines the reasoning power of LLMs with the ability to access external knowledge, solving the problems of outdated training data, hallucinations, and lack of private knowledge.

How It Works

1. User asks a question (Prompt) 2. Question is converted to an embedding vector 3. Vector DB finds semantically similar documents 4. Retrieved context is added to the prompt 5. LLM generates an answer grounded in the context

Use Cases

  • •Customer support chatbots with company knowledge
  • •Document Q&A systems
  • •Internal search over company data
  • •Research assistants with citation

Real-World Examples

Perplexity

AI search that retrieves and cites web sources

NotebookLM

Google's document-grounded AI assistant

ChatPDF

Upload PDFs and ask questions

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