Definition

RAG is a way to improve AI by combining two steps: first, it searches for real information from outside sources (retrieval), then it uses that info to write an answer (generation). This helps the AI be more accurate and up to date.

Example

When you ask an AI a question about a recent event, RAG helps it find facts online and write a better answer.

How It’s Used in AI

RAG is used in advanced chatbots, customer support tools, and research assistants. It’s great for answering specific or up-to-date questions, pulling facts from company documents, websites, or databases before generating a reply.

Brief History

RAG was introduced by Facebook AI Research in 2020. It became popular as a way to reduce hallucinations in LLMs and allow them to access current, domain-specific knowledge.

Key Tools or Models

Popular RAG tools include LangChain, LlamaIndex, Pinecone, and Weaviate. RAG is often powered by LLMs like GPT-4, paired with vector databases and semantic search.

Pro Tip

RAG improves trust in AI responses—but only if the sources it pulls from are accurate. Always use clean, high-quality data.