RAG (Retrieval-Augmented Generation)

Retrieval-Augmented Generation (RAG) is a technique that enhances AI responses by first retrieving relevant information from a knowledge base before generating an answer. This combines the language capabilities of LLMs with the accuracy of specific, up-to-date information.

In RAG systems, when a user asks a question, the system first searches a database of documents to find relevant content, then provides that content as context to the LLM, which generates a response grounded in the retrieved information.

RAG is particularly valuable for AI applications that need accurate, current information, as it overcomes the limitation of LLMs only knowing what was in their training data. It's widely used in enterprise chatbots, customer support systems, and AI search engines.

Example: Perplexity AI uses RAG to search the web for current information before answering questions, which is why it can provide answers about recent events that weren't in the model's training data.

In SeCoFlow: Understanding RAG helps you optimize content for AI search engines that use this technique, as well-structured, authoritative content is more likely to be retrieved and cited.