Overview
This project builds a domain-specific Q&A assistant backed by retrieval-augmented generation (RAG). The assistant answers questions about regulatory guidance for AI/ML in medical devices by retrieving relevant information from a curated knowledge base of FDA guidance documents and generating informed responses.
The implementation uses OpenAI's Agent Builder as the primary platform, providing a complete environment for agent workflow design, built-in vector storage for document ingestion and retrieval, and a front-end framework, letting the team focus on knowledge base curation, retrieval configuration, and prompt engineering rather than infrastructure.
Key Concepts
- How RAG works: document ingestion, embedding, vector retrieval, and grounded generation
- Knowledge base curation: selecting, organizing, and preparing domain-specific source documents for ingestion
- Prompt engineering for Q&A accuracy: system instructions, retrieval tuning, and grounding techniques to minimize hallucination
- Agent deployment on a commercial platform: configuring and shipping an assistant using OpenAI Agent Builder
Deliverables
A deployed, functional Q&A assistant that answers questions about AI/ML regulatory frameworks for medical devices using a curated knowledge base. Demonstrates both the ability to ship an AI-powered information tool end-to-end, and domain expertise in navigating complex regulatory guidance.
Applied Skills
- RAG architecture fundamentals (document ingestion, embedding, vector retrieval, grounded generation)
- Knowledge base curation and document preparation for domain-specific retrieval
- Prompt engineering for Q&A accuracy and hallucination reduction
- Agent deployment using a commercial platform (OpenAI Agent Builder)