Overview
Medical Assistant is a Retrieval-Augmented Generation (RAG) based AI solution designed to address information overload in healthcare settings. By leveraging medical manuals as a knowledge base, the system helps healthcare professionals quickly retrieve relevant medical information and generate accurate, context-aware responses to clinical questions.
Business Problem
Healthcare professionals face overwhelming volumes of medical literature and documentation. Finding relevant information quickly is critical for patient care, but manual searches through medical manuals and reference materials are time-consuming and prone to missing critical details.
Approach
- Knowledge Base: Medical manuals and reference documents were ingested and vectorized for semantic search.
- Retrieval: Relevant passages are retrieved based on the user query using vector similarity search.
- Generation: A Large Language Model (LLM) generates a response grounded in the retrieved context, reducing hallucinations.
- Prompt Engineering: Carefully designed prompts ensure responses are accurate, cite sources, and maintain appropriate medical disclaimers.
Key Techniques
- Retrieval-Augmented Generation (RAG) architecture
- Vector embeddings for semantic document search
- Prompt engineering for clinical accuracy and safety
- Context window management for large medical documents
Tech Stack
RAG LLM Prompt Engineering