Retrieval Augmented Generation

Retrieval Augmented Generation (RAG) is an AI framework that enhances large language models (LLMs) by integrating external knowledge retrieval. It works by first retrieving relevant information from a knowledge base (like documents or databases) and then using that context to generate more accurate, up-to-date, and factually grounded responses. This approach reduces hallucinations and improves the reliability of AI-generated content by grounding it in verifiable sources.

Also known as: RAG, Retrieval-Augmented Generation, Retrieval Augmented Generation (RAG), RAG framework, Retrieval-augmented LLM

Why learn Retrieval Augmented Generation?

Developers should learn RAG when building applications that require factual accuracy, domain-specific knowledge, or up-to-date information beyond an LLM's training data, such as chatbots, question-answering systems, or content generation tools. It's particularly useful for mitigating LLM limitations like outdated knowledge or lack of access to proprietary data, enabling more trustworthy and context-aware AI solutions in fields like customer support, research, or enterprise documentation.

Learning Resources

• docs: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
• tutorial: LangChain RAG Tutorial
• docs: Building RAG Applications with LlamaIndex
• video: RAG Explained - YouTube Video by Cohere
• course: Retrieval-Augmented Generation (RAG) - Coursera Course