Retrieval-Augmented Answer Engine for Clinical Reference Documents

Added Jun 2025 3 design docs

General-purpose AI assistants are unusable for serious healthcare questions because their answers cannot be traced to authoritative sources. Clinics and health organizations hold exactly the documents that should ground such answers, including internal protocols, clinical references, and patient-education material, but need a system that confines the AI to that corpus. Retrieval-augmented generation is the architecture that makes this possible, and building it well over medical content is a benchmark skill for AI engineers. The intern builds a complete RAG application for the healthcare domain as a Python FastAPI service. The pipeline combines LangChain for orchestration with LlamaIndex for document ingestion and indexing: medical reference documents are chunked, embedded, and stored for semantic retrieval, and incoming questions trigger retrieval of the most relevant passages before generation. The system supports both OpenAI and Llama models as the generation backend, and the intern implements comparison plumbing to evaluate grounding quality across both. Answers carry citations back to source passages, and the intern tunes chunking, retrieval depth, and prompt constraints to minimize unsupported claims. The project demonstrates end-to-end RAG construction, dual-framework integration, model-backend flexibility, and the evidence-first mindset that domain-specific AI requires.

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