The original title is: "RAG Explained Simply: How AI Finds the Right Information Before Answering"

Short summary

A beginner-friendly explanation of Retrieval-Augmented Generation (RAG) that covers how AI retrieves relevant external information before answering questions. The article walks through converting structured data into text, creating embeddings with a shared model, storing vectors in a vector database with metadata, and performing semantic search to find conceptually related records. Key point: RAG updates accessible knowledge at runtime without retraining the model, and semantic search catches related concepts that keyword search misses.

• •RAG retrieves relevant external info at runtime and places it in the LLM context window — no retraining needed
• •Embeddings convert text to vectors where similar meanings cluster together, enabling semantic search beyond keyword matching
• •Vector DB stores embeddings with metadata for filtering; source databases remain the system of record

Generated with AI, which can make mistakes.