RAG Explained: Retrieval-Augmented Generation pipeline for grounded LLM responses

Short summary

RAG (Retrieval-Augmented Generation) solves the problem of LLMs hallucinating when asked about private data by retrieving relevant document chunks and feeding them as context before generation. The article walks through the two-phase pipeline: an ingestion phase (document loading, chunking, embedding, vector storage) and a query-time phase (retrieval, prompt augmentation, generation). It frames RAG as shifting the LLM's role from 'know everything' to 'read and reason over provided context,' making answers auditable and grounded.

• •RAG retrieves relevant document chunks before generation to ground LLM answers in real data
• •Two distinct phases: ingestion (loading, chunking, embedding) and query-time (retrieval, augmentation, generation)
• •RAG doesn't eliminate hallucination but makes answers traceable to specific retrieved chunks

Generated with AI, which can make mistakes.