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Course guide

What is RAG? Retrieval-Augmented Generation Explained

RAG retrieves relevant documents and injects them into LLM prompts, reducing hallucinations and enabling domain-specific AI. Learn how production RAG systems work.

Retrieval-Augmented Generation (RAG) is an AI architecture pattern that enhances Large Language Model responses by retrieving relevant information from a knowledge base before generating an answer.

Instead of relying solely on the model's training data (which can be outdated or incomplete), RAG injects real, current, domain-specific documents into the prompt. This dramatically reduces hallucinations and enables AI applications that answer questions about YOUR data - company docs, product manuals, legal contracts, medical records.

How RAG Works

  1. Index: Documents are chunked, embedded into vectors, and stored in a vector database
  2. Retrieve: When a user asks a question, the query is embedded and the most similar document chunks are retrieved
  3. Augment: Retrieved chunks are injected into the LLM prompt as context
  4. Generate: The LLM generates an answer grounded in the retrieved documents

Learn RAG Engineering - Free

Our free Production-Grade RAG Systems Engineering course teaches you to build scalable, reliable RAG systems - not toy demos. 16 modules, 31 labs.

Learning path

Continue from concept to implementation

These course modules place this topic in context, connect it to adjacent decisions, and provide the practical next step.

Module 1

Introduction to AI & RAG Systems

LLM fundamentals, hallucinations, and why retrieval-augmented generation changes everything

3 hours | 4 learning objectives

Module 2

Foundations of Search & Retrieval

BM25, TF-IDF, vector search fundamentals, and similarity metrics

3 hours | 4 learning objectives

Module 3

Embeddings Deep Dive

Embedding models, optimization strategies, and choosing the right model for your use case

3 hours | 4 learning objectives

Practical outcomes

What the recommended module teaches

  • Understand how LLMs work at a high level
  • Learn about tokens, context windows, and their limitations
  • Understand why LLMs hallucinate and how RAG solves it
  • Compare vanilla LLM vs RAG responses