How to Build a RAG System with Your Own Documents in 7 Simple Steps

Short summary

Learn to build a RAG system that answers questions from your private documents using free or freemium tools like Pinecone and OpenAI. The guide walks through document chunking, embedding, vector storage, and deploying to Slack or web UI without hiring data scientists. Common pitfalls like oversized chunks and poor overlap are explained with specific fixes.

• •RAG combines a retriever (vector database) and generator (LLM) to answer questions grounded in your own documents
• •Step-by-step tutorial covers chunking, embedding, vector storage, and deployment using free/freemium services
• •Avoid common mistakes: use vector stores over generic search, properly chunk with overlap, respect token limits

Generated with AI, which can make mistakes.