Search for: All records

In closed-domain Question Answering (QA), Large Language Models (LLMs) often fail to deliver responses specialized enough for niche subdomains. Broadly trained models may not capture the nuanced terminology and contextual precision required in these fields, which frequently lack domain-specific conversational data and face computational constraints. To address this, we propose a methodology leveraging a Retrieval-Augmented Generation (RAG) framework that integrates data extraction with fine-tuning using domain-specific question-answer pairs. Our approach employs Question-Answer Generation (QAG) to create tailored training datasets, enabling fine-tuned models to incorporate specialized jargon and context while remaining computationally accessible to domain experts. To exemplify this methodology, we demonstrate its application within the medical domain through a case study centered on the creation of a dementia care chat assistant. A significant benefit of this approach lies in its ease of replication across various domains and scalability for integration into diverse user groups, making it a versatile solution for enhancing chat assistants.