What is RAG? Build Your First Retrieval Augmented Generation App

The Problem with LLMs

LLMs do not know about your internal documents, recent events, or private data. RAG solves this by retrieving relevant context before generating answers.

How RAG Works

1. Embed your documents into vectors using an embedding model
2. Store vectors in a vector database (Pinecone, Weaviate, Chroma)
3. Search for similar vectors when a user asks a question
4. Generate an answer using the retrieved context + LLM

Use Cases

• Customer support bots trained on your docs
• Legal document Q&A
• Internal company knowledge base search
• Personalized learning assistants

Tools to Get Started

LangChain, LlamaIndex, or CrewAI — all three simplify RAG pipelines. Use OpenAI or Claude as the LLM and Chroma for local vector storage.

Next Step

Start with a small PDF, embed it, and build a simple Q&A bot. It is surprisingly easy once you try.