RAG is the technique of feeding a language model your own documents so it answers from them instead of from what it memorized. It first retrieves the fact from your base and then writes the reply. It is how you get a hotel chatbot to answer with its real rates and policies rather than making them up.
Example With RAG, the assistant answers the breakfast time by reading the hotel manual, not guessing.
RAG stands for Retrieval-Augmented Generation, a technique that combines fetching information from your own database with text generation from a language model. Instead of the model making up an answer, it first searches for real documents, like hotel rates or cancellation policies, and then drafts a response based on those documents.
How does RAG work?
RAG works in two steps: first, a search engine finds the relevant snippets in your documents, such as a PDF with room prices, then the language model uses only those snippets to write a coherent answer. For example, if a guest asks about a suite cost, the chatbot extracts the exact rate from your document and presents it without hallucinations.
What is RAG used for and how is it different from a normal language model?
RAG is used to make an AI system answer with up-to-date and business-specific information without fabricating data. Unlike a traditional language model that only repeats what it learned during training, which may be outdated or generic, RAG queries your knowledge base in real time, such as a hotel's cancellation policies, ensuring accurate and verifiable responses.
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