A Retrieval-Augmented Generation Framework for Medical Question Answering: Design, Implementation, and Evaluation of an AI-Driven Healthcare Chatbot

• Author(s): Swarnava Banerjee; Anindita Bhattacharjee; Apurba Paul
• Paper ID: 1717085
• Page: 3632-3638
• Published Date: 01-05-2026
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 10 April-2026

Abstract

The use of artificial intelligence systems in delivering evidence-based medical knowledge to patients is one promising avenue. Despite their popularity, existing large language models (LLMs) tend to suffer from hallucinations and outdated knowledge, which poses serious risks when such tools are used in the sensitive healthcare industry. In this work, we explore a retrieval-augmented generation (RAG) architecture combining FAISS-based dense vector retrieval with the Llama-3.3-70B language model, coordinated through the LangChain framework, to overcome these disadvantages. Specifically, our method involves the encoding of a selected collection of medical literature into the 384-dimensional dense vector space using the all-MiniLM-L6-v2 sentence transformer model. These vectors get stored in a FAISS flat-L2 store, after which they are retrieved during inference and used by the LLM as context when generating answers in order to improve their relevance and accuracy. We also apply a constraint on the type of prompts given to the LLM in order to restrict the responses to only medical information. Evaluation done qualitatively and quantitatively confirms the effectiveness of our approach in providing high-quality and reliable medical.

Keywords

Retrieval-Augmented Generation (RAG), Large Language Models, Healthcare Chatbot, FAISS, LangChain, Natural Language Processing, Medical AI

Citations

IRE Journals:
Swarnava Banerjee, Anindita Bhattacharjee, Apurba Paul "A Retrieval-Augmented Generation Framework for Medical Question Answering: Design, Implementation, and Evaluation of an AI-Driven Healthcare Chatbot" Iconic Research And Engineering Journals Volume 9 Issue 10 2026 Page 3632-3638 https://doi.org/10.64388/IREV9I10-1717085

IEEE:
Swarnava Banerjee, Anindita Bhattacharjee, Apurba Paul "A Retrieval-Augmented Generation Framework for Medical Question Answering: Design, Implementation, and Evaluation of an AI-Driven Healthcare Chatbot" Iconic Research And Engineering Journals, 9(10) https://doi.org/10.64388/IREV9I10-1717085