# The Geometry of Queries: Query-Based Innovations in Retrieval-Augmented Generation for Healthcare QA

Source: [The Geometry of Queries: Query-Based Innovations in Retrieval-Augmented Generation for Healthcare QA](<https://www.sota2.com/research/paper/68fa7a441e209131cea82093>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T05:16:12.275Z

Authors: Eric Yang, Jonathan Amar, Jong Ha Lee, Bhawesh Kumar, Yugang Jia

Published: 2024-07-25T13:47:01.000Z

## Abstract

Deploying Large Language Models (LLMs) for healthcare question answering requires robust methods to ensure accuracy and reliability. This work introduces Query-Based Retrieval Augmented Generation (QB-RAG), a framework for enhancing Retrieval-Augmented Generation (RAG) systems in healthcare question-answering by pre-aligning user queries with a database of curated, answerable questions derived from healthcare content. A key component of QB-RAG is an LLM-based filtering mechanism that ensures that only relevant and answerable questions are included in the database, enabling reliable reference query generation at scale. We provide theoretical motivation for QB-RAG, conduct a comparative analysis of existing retrieval enhancement techniques, and introduce a generalizable, comprehensive evaluation framework that assesses both the retrieval effectiveness and the quality of the generated response based on faithfulness, relevance, and adherence to the guideline. Our empirical evaluation on a healthcare data set demonstrates the superior performance of QB-RAG compared to existing retrieval methods, highlighting its practical value in building trustworthy digital health applications for health question-answering.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2407.18044>)

[PDF](<https://arxiv.org/pdf/2407.18044.pdf>)
