From Fine-Tuning to Grounding: Retrieval-Augmented Generation for Biomedical LLMs in Research and Clinical Data Infrastructures

Large language models (LLMs) are increasingly explored for biomedical research and clinical medicine, yet their use remains limited by hallucinations, missing provenance, domain drift, privacy constraints, and uncertain regulatory pathways. Retrieval-augmented generation (RAG) offers a pragmatic alternative to full model training or fine-tuning by grounding LLM outputs in curated, versioned, and auditable external sources. This narrative review examines RAG as a biomedical grounding infrastructure rather than a chatbot add-on. We clarify key terminology around databases, knowledge bases, vector stores, knowledge graphs, RAG systems, and grounding, and distinguish factual, contextual, analytical, provenance, normative, and operational grounding. Two anchor scenarios are used to structure the review: single-cell annotation and omics interpretation as an exploratory biomedical research setting, and EHR, PDF, and clinical free-text integration as a regulated clinical information setting. We compare how these scenarios differ in sources, retrieval units, risk profiles, evaluation targets, infrastructure needs, and governance requirements. We further discuss design choices across the biomedical RAG lifecycle, evaluation and benchmarking, semi-automated and agentic RAG construction, infrastructure integration, normative grounding, and deployer-side governance. We argue that biomedical LLM deployment will not be determined by model scale alone, but by the quality of grounding infrastructures that make outputs traceable, contextual, updateable, and accountable.