RAG stands for Retrieval-Augmented Generation. It's a method for improving the accuracy and relevance of Large Language Models. An LLM is a type of AI that can generate human-like text, but its knowledge is limited to the data it was trained on. This means it can't access real-time information and may sometimes make up information.
RAG solves this problem by giving the LLM an external knowledge base to work from. When a user asks a question, the RAG system first searches a specific, reliable source like a company's internal documents or a recent database to find relevant information. Then, it uses this retrieved information to guide the LLM's response. This ensures the answer is both accurate and current, reducing the chance of errors.
The process works in two main steps. First, the "retrieval" step is all about finding information. The user's query is used to search a database of documents, and the most relevant ones are pulled out. Second, the "generation" step is where the LLM does its work. The retrieved documents, along with the original question, are fed into the LLM as a single prompt. The model then synthesizes this information to create a coherent answer, based on the facts from the retrieved data. This two-step process allows the AI to provide verifiable answers without the need for constant, expensive retraining on new data.
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