98. RAG: Give Your AI Access to Your Documents

The article explains retrieval-augmented generation (RAG) as a way to improve LLM accuracy by pulling relevant documents into the prompt before generating an answer.

It contrasts RAG with fine-tuning, noting that RAG is better for using fresh, private, or domain-specific knowledge without retraining the model.

The piece outlines the core pipeline: index documents, retrieve the most relevant chunks, then generate grounded responses from that context.

It also covers practical implementation details such as chunking strategies, vector embeddings, vector databases, and common tooling like LangChain and sentence-transformers.