What is RAG? (Retrieval-Augmented Generation Explained)

What is RAG? In this episode of "What is," Quinton Wall breaks down Retrieval-Augmented Generation and why foundation LLMs like Claude, GPT, and Gemini need it to solve the training cutoff and hallucination problem. LLMs are frozen in time by their training data. When you ask about private internal docs, changing product specs, or live pricing, the model either hallucinates or fails. RAG fixes this by fetching relevant data at runtime and feeding it directly into the context window so the model answers from verified grounding, not guesses. In This Video: • The Core Problem: Why frozen training cutoffs cause hallucinations • What RAG Means: Retrieval-Augmented Generation defined • The 3-Step Pipeline: Chunking, Vector Embeddings, and Database Storage (Pinecone, Qdrant, pgvector) • Semantic Search vs Keyword Matching: Finding meaning instead of exact words • Real-World Architecture: How RAG powers production systems like My Camino Guide #WhatIs #RAG #MachineLearning #AIEngineering #DeveloperTools #Postman #LLM #VectorDatabase #SoftwareArchitecture