Fine-tuning and RAG are the two main ways to give an AI model specific knowledge. Fine-tuning retrains the model on custom examples, while RAG (retrieval augmented generation) fetches relevant documents at question time and lets the model answer from them.
For hotel use cases, RAG usually wins. Property information changes constantly, rates, hours, policies, and RAG systems answer from the current knowledge base without retraining, and can cite their source. Fine-tuning suits style and format learning more than facts.
The practical question for buyers is simpler than the jargon, when the hotel updates a policy, how fast does the assistant's answer change, and can it show where an answer came from. Systems that answer from retrieved documents pass both tests naturally.
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