What is retrieval-augmented generation used for?#
By connecting the AI to a trusted knowledge base (like your company's internal documents), this technique ensures responses are up-to-date, factually grounded in your data, and relevant to your business, reducing the risk of inaccurate answers.
How does retrieval-augmented generation work?#
When a user asks a question, the system first searches a pre-defined library of documents—such as your product manuals, HR policies, or recent reports—to find passages that are relevant to the query. This search-and-find step is called "retrieval."
The system then takes the user's original question and combines it with the relevant information it just found. This combined package is given to the AI language model as context. The model uses this specific, provided information to "augment" its built-in knowledge and generate a precise, well-supported answer based on facts, not just its general training.
How is RAG different from a standard chatbot?#
Retrieval-Augmented Generation provides current, verifiable answers from a specific set of documents. Unlike a standard AI chatbot like the public version of ChatGPT, its knowledge is not limited to its original training data, so it can provide real-time information and cite its sources. Unlike a traditional keyword search that just returns a list of links, it synthesizes information from those sources into a direct, conversational answer.