What Is RAG? A Complete Guide to Retrieval‑Augmented Generation for AI Engineers
This article explains Retrieval‑Augmented Generation (RAG), covering why large language models need external knowledge, the full offline‑and‑online workflow, document chunking, embedding evolution, vector database choices, multi‑path retrieval, evaluation metrics, hallucination types, and practical strategies to mitigate them.
