This is from ChatGPT.

RAG stands for Retrieval-Augmented Generation, and it’s a clever AI workflow that bridges the gap between static language models and up-to-date, external information. Most large language models (LLMs), like GPT, are trained on a fixed dataset and can’t access new information after their training cutoff. This can be a problem when users want answers about recent events or niche topics that weren’t in the training data. RAG solves this by combining a language model with a retrieval system—allowing the model to fetch relevant information from external sources and generate responses based on that.

How It Works

In a typical RAG workflow, when you ask a question, the system first sends that query to a retriever—often a vector database or search engine that scans a collection of documents (web pages, PDFs, internal knowledge bases, etc.) for relevant information. The top results are then fed into the generator, which is the language model. The model uses both the query and the retrieved documents to produce a coherent and contextually accurate answer. This setup boosts the model’s reliability, especially for applications like customer support, legal research, or enterprise knowledge systems where hallucinations or outdated answers are unacceptable.

Why RAG Matters

RAG is quickly becoming a foundational design pattern for AI agents, chatbots, and knowledge apps. It’s what enables tools like ChatGPT with “web browsing” to cite sources or answer questions about current events. In enterprise settings, RAG lets teams build AI assistants that draw on internal documents—like wikis, help centers, or contracts—without needing to retrain the model. It offers the best of both worlds: the fluency of large language models and the factual grounding of real-time data. As the need for trustworthy and up-to-date AI responses grows, expect RAG to be at the heart of many next-generation AI workflows.