RAG beats fine-tuning for construction docs

This is a solid real-world RAG use case, but it only works well if the system behaves more like a grounded search assistant than an all-knowing expert.

• –For manuals and standards, retrieval quality matters more than fine-tuning because users need exact sections, citations, and version-aware answers
• –Complex questions can work when the system pulls multiple relevant passages, but edge cases still risk hallucinations or overconfident synthesis
• –Version control is a major limitation because outdated codes, superseded specs, and jurisdiction-specific rules can quietly poison answers
• –The best implementation would combine semantic search, strict source attribution, and user-visible references rather than free-form responses
• –This is useful for AI developers building vertical assistants, but it is still closer to knowledge infrastructure than true domain reasoning