Clinical trials generate massive volumes of documentation, from protocols and informed consent forms to regulatory submissions spanning years of work. Traditional keyword search systems miss critical connections because they can't understand that "hepatic impairment," "liver function test abnormalities," and "elevated AST/ALT" all describe the same clinical domain. Retrieval-augmented generation (RAG) combined with vector search solves this by indexing documents as semantic embeddings, retrieving relevant passages based on meaning rather than exact word matches, and feeding those passages to a language model to ground its output in verified sources. Recent studies show RAG systems correctly identifying protocol compliance gaps against FDA guidance, achieving over 87% accuracy in patient-to-trial matching tasks, and reducing hallucination rates by more than 40% compared to standard AI baselines. For site staff screening patients or sponsors writing protocols, this means faster access to the right precedent language, eligibility criteria from similar trials, and regulatory guidance passages without manual keyword hunting. The architecture is already in use for protocol development, patient pre-screening, and regulatory document generation, with every AI-generated claim traceable back to a specific source document rather than produced from memory alone.