[2026 Latest] Transforming Property-Specific Knowledge into AI Assets Using RAG

With the surge in inbound tourism, hospitality staff are reaching their limits in handling multilingual inquiries. In particular, answering questions about property-specific knowledge—such as unique late checkout rules or the availability of hidden local restaurants—which do not exist in generic internet data, has become a significant burden for staff. In this article, we provide a practical perspective on how to utilize RAG (Retrieval-Augmented Generation) to suppress AI hallucinations and provide highly accurate information unique to each accommodation facility.

Why Generic AI Alone Falls Short of True Hospitality

While generic Large Language Models (LLMs) like ChatGPT are capable of highly fluent multilingual dialogue, implementing them as-is in a hospitality setting carries significant risks. In our actual support cases, we have seen instances where LLMs hallucinate "public bath hours" that don't exist or confuse rules with those of other facilities.

This happens because LLMs generate "plausible-sounding text" based on their training data. For "ultimate hospitality" in accommodation facilities, accurate facts specific to that property are essential, rather than vague answers. To ensure the AI correctly recognizes your unique rules, building a RAG system that references external information is a more realistic solution in terms of both cost and accuracy than retraining (fine-tuning) the model.

The High-Precision Response Mechanism Enabled by RAG (Retrieval-Augmented Generation)

RAG (Retrieval-Augmented Generation) is a technology that, in response to a user's question, first searches for relevant information from internal databases (such as PDF facility manuals or Excel lists of local sightseeing spots) and then has the AI generate an answer based on that information.

The greatest advantage of this method is that it dramatically reduces hallucinations because the AI follows a process of "searching the database for what it doesn't know." Furthermore, updating information is as simple as updating the database, allowing for immediate responses to seasonal event information or sudden facility maintenance notices.

For example, in our Own EC Site Construction & Growth Support services, converting product manuals into RAG knowledge bases has dramatically improved the automated response rate for technical inquiries. This follows the exact same logic as automating "facility guidance" in accommodation facilities.

Key Principles of Knowledge Structuring in Practical Support Settings

The accuracy of RAG depends more on the "quality of the input data" than on the performance of the AI itself. A common pattern we see is uploading PDFs that are simply scans of old paper documents, leading to frequent incorrect answers because the AI cannot recognize the text correctly. In our support work, we start with "data cleansing," which involves appropriately dividing information into units called "chunks" and organizing them into a format that is easy for the AI to search.

Particularly for inbound support, setting up dictionaries to eliminate discrepancies when translating Japanese-specific expressions (e.g., ensuring "omeshimono" is interpreted as "clothing") into multiple languages is crucial. This enables the provision of polite and accurate information characteristic of Japanese hospitality, regardless of whether the inquiry is in English, Chinese, Korean, or any other language.

The Correlation Between Multilingual Support Cost Reduction and Customer Satisfaction

By introducing an AI concierge, the time front desk staff spend tied up with simple questions (Wi-Fi passwords, breakfast venue locations, etc.) is significantly reduced. The following chart visualizes the automation rate of inquiry handling through RAG utilization and the corresponding trend in human response time.

The most significant change we observe during our support is the "psychological breathing room" for staff. Freed from the stress of language barriers, staff can focus on more sophisticated "face-to-face hospitality." This structure is common to our Own EC Site Construction & Growth Support, where automating routine work allows more time to be dedicated to strategic planning.