what is rag in ai

What is RAG in AI? Learn about its utility and importance

If you're interested in the field of Artificial Intelligence (AI), you've likely heard questions like "what is rag in AI" recently. Here, RAG stands for Retrieval-Augmented Generation. It's an AI architecture used to enhance the power of generative AI models, such as ChatGPT or other Large Language Models (LLMs).

Simply put, when a user asks an AI a question, a traditional LLM provides a response based on its training data. However, a RAG model not only uses its previously learned knowledge but also retrieves (i.e., searches) new and fresh data in real time to generate an answer. This makes answers more relevant and up-to-date.

RAG consists of two main components: a Retriever, which searches for information from databases, the web, or other sources, and a Generator, which generates answers in natural language based on that information. This process increases both the quality and accuracy of AI responses.

For example, if a bank's website has a RAG-based chatbot, it can instantly search for new interest rates or offers from online documents, rather than just from old data. This is why RAG is making AI increasingly useful in the corporate, healthcare, education, and technology sectors today.

Useful Tip:

If you want to build a smart and up-to-date LAML-based chatbot for your business or website, adopting the RAG architecture is a great option. This will provide your users with up-to-date and accurate information, increasing their trust and satisfaction.