What Is RAG (Retrieval-Augmented Generation)
Definition
RAG, short for Retrieval-Augmented Generation, is a technique that connects a language model (LLM) to your own knowledge base. Instead of answering only from what it learned during training, the system first retrieves relevant information from documents, databases, or specific systems, and then generates its answer based on that retrieved material.
Its biggest advantage is accuracy: by grounding every answer in real, current information, RAG dramatically reduces so-called hallucinations, the cases where AI makes up facts. For a hotel, this means the assistant can answer about current rates, restaurant hours, cancellation policies, or the specific amenities of your property, citing your own information instead of generalities. It also makes updating what the AI knows as simple as updating the source documents, with no need to retrain the model.
How to leverage it
- Ensures the assistant answers with the hotel's real information (rates, hours, policies, amenities) rather than generic or invented data.
- Lets you update the AI's knowledge by editing documents or connecting the PMS, with no need to retrain any model.
- Reduces hallucinations and errors, increasing guest trust in automated answers.
- Makes it easy to deliver consistent information across every channel, since the assistant draws from a single source of truth about your property.
How WeSpeak helps with RAG (Retrieval-Augmented Generation)
WeSpeak uses RAG so its AI assistant always answers with your hotel's real, up-to-date information, not generic data. We connect the language model to your rates, policies, and services, so every guest gets accurate, reliable answers. And when something changes at your hotel, you simply update the source and the assistant knows it instantly. See how WeSpeak combines the power of AI with the accuracy of your own data.
Learn more: AI chatbot for hotels
Book a free demo
