Building Your First RAG Application: A Practical Guide

RAG (Retrieval-Augmented Generation) lets AI answer questions about your specific documents instead of just general knowledge. It's the technology behind most "chat with your docs" tools, and building your own is surprisingly accessible.

The architecture has three parts: document processing (chunking and embedding your content), retrieval (finding relevant chunks for a query), and generation (having an LLM answer using retrieved context). Each step has decisions that affect quality dramatically.

For chunking, smaller is usually better—500-1000 characters per chunk with overlap. For embeddings, OpenAI's ada-002 or Cohere's models work well for most use cases. For retrieval, start with simple cosine similarity; you can add reranking later if needed. For generation, the prompt matters enormously: explicitly tell the model to only use provided context and to admit when information isn't available.