County 911 records analysts need grounded answers from the CAD policy corpus with citations, not free-form generation. Which workload design meets that need?

County 911 records analysts require answers grounded in the CAD policy corpus with citations, not unconstrained free-form generation. RAG is the workload design that retrieves CAD policy passages first and then generates answers that can cite those passages, aligning generation with authoritative municipal text. Fine-tuning a language model on CAD policies with cited answers generated from weights still leaves the system generating from memorized weights rather than retrieved passages, so any citations are invented and vulnerable to hallucinating policy language that never appeared in the corpus, and fine-tuning cycles cannot keep pace with policy changes that need immediate citation validity. Ranking CAD policy passages in a vector store to generate cited answers from similarity mistakes retrieval ranking for grounded generation, since similarity scores alone cannot produce answers that cite specific policy passages, so analysts still would not receive grounded, citation-ready responses tied to authoritative text. Condensing CAD policy corpus into summary context for cited generated answers loses the original document boundaries, so the model cannot point analysts to specific citable policy passages even when the summary is accurate, and any citations would point to the lossy summary rather than real document locations. Exam caveat: citation-capable RAG depends on retrieving the right passages and presenting them honestly; retrieval failure or over-compression of context can still produce misleading answers even when the architecture is correct. Operational check: verify that sample analyst queries return passages from the approved CAD policy set, that citations resolve to real document locations, and that ungrounded generative paths are not used for policy-sensitive 911 records work.