RAG Architecture

RAG stands for Retrieval-Augmented Generation — a technique used in AI to give more accurate and up-to-date answers. Instead of the AI only using what it already “knows” It first searches for relevant information Then uses that information to generate a better answer You can build a RAG app by combining 3 core pieces: Store knowledge from your documents in a searchable form Retrieve relevant chunks for a user question Ask an LLM to answer using only that retrieved context Think of it as: search first, generate second . The model gets smarter for your data without retraining—very budget-friendly, very civilized. What a RAG app needs A typical RAG pipeline looks like this: The basic architecture 1) Data ingestion Load your source data: PDFs Word docs Markdown files HTML/web pages database records internal knowledge base 2) Chunking Split large documents into smaller passages, for example: 300–800 tokens per chunk some overlap, like 50–100 tokens Why? Because embedding and retrieval work