  Digital health chatbots powered by Large Language Models (LLMs) have the
potential to significantly improve personal health management for chronic
conditions by providing accessible and on-demand health coaching and
question-answering. However, these chatbots risk providing unverified and
inaccurate information because LLMs generate responses based on patterns
learned from diverse internet data. Retrieval Augmented Generation (RAG) can
help mitigate hallucinations and inaccuracies in LLM responses by grounding it
on reliable content. However, efficiently and accurately retrieving most
relevant set of content for real-time user questions remains a challenge. In
this work, we introduce Query-Based Retrieval Augmented Generation (QB-RAG), a
novel approach that pre-computes a database of potential queries from a content
base using LLMs. For an incoming patient question, QB-RAG efficiently matches
it against this pre-generated query database using vector search, improving
alignment between user questions and the content. We establish a theoretical
foundation for QB-RAG and provide a comparative analysis of existing retrieval
enhancement techniques for RAG systems. Finally, our empirical evaluation
demonstrates that QB-RAG significantly improves the accuracy of healthcare
question answering, paving the way for robust and trustworthy LLM applications
in digital health.
