Getting Started with RAG: A Practical Guide

Retrieval-Augmented Generation (RAG) has become the default pattern for building AI applications that need up-to-date, domain-specific knowledge. Instead of relying solely on a model's training data, RAG retrieves relevant documents at query time and feeds them into the prompt.

The basic pipeline is straightforward: ingest documents, chunk them into manageable pieces, embed each chunk into a vector store, and at query time retrieve the most relevant chunks to include in your prompt context.

Start with a simple stack: LangChain or LlamaIndex for orchestration, OpenAI embeddings, and a vector store like Pinecone, Weaviate, or even Chroma for local development. Don't over-engineer on day one.

Chunk size matters more than most people think. Too small and you lose context; too large and retrieval precision drops. A good starting point is 500–1000 tokens with 10–20% overlap between chunks.

Evaluation is non-negotiable. Build a small set of question-answer pairs from your domain and measure retrieval accuracy and answer quality before shipping. Tools like RAGAS or a simple LLM-as-judge loop work well for this.

Once your baseline works, optimize incrementally: hybrid search (keyword + semantic), reranking with a cross-encoder, and metadata filtering are the highest-leverage improvements for most use cases.