RAG Systems Explained: A Developer's Guide

Retrieval-Augmented Generation (RAG) has become the standard approach for building AI applications that need to work with custom data. Here's everything you need to know.

RAG works by first retrieving relevant documents from a knowledge base, then using those documents as context for the language model to generate a response.

The key components are: a vector database for storing and searching embeddings, an embedding model for converting text to vectors, and a language model for generating responses.

Chunking strategy matters more than most people realize. How you split your documents affects retrieval quality. Experiment with different chunk sizes and overlaps.

Evaluation is critical. Build a test set of questions and expected answers, then measure retrieval accuracy and response quality as you iterate on your system.