RAG Strategies for a Hallucination-Free University Admission AI Assistant
RAG Strategies for a Hallucination-Free University Admission AI Assistant

Author(s): Venko Andonov
Subject(s): Economy, Business Economy / Management, ICT Information and Communications Technologies, Socio-Economic Research
Published by: Университет за национално и световно стопанство (УНСС)
Keywords: Retrieval augmented generation; University AI
Summary/Abstract: The development of a LLM-based university admission assistant requires the use of up-to-date and factually correct information. The state of the art proprietary and open source models are prone to hallucination (generating false information because of a lack of specific real information in their pretraining phase), which might cause confusion for the prospective students. A set of retrieval augmented generation (RAG) strategies on different models are evaluated, that ensure that the models output up-to-date and factually correct information and are also able to use personalized data to help the prospective students with the application process. We are using a specific case for UNWE, Sofia, which also adds an additional requirement for the support of Bulgarian language in the conversation.

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