Domain-Specific AI vs General AI

The distinction between domain-specific AI (trained on construction documents, building codes, and AEC workflows) and general-purpose AI (ChatGPT, Gemini, Claude) is the single most important concept for AEC firms evaluating AI tools. General AI knows about construction the way a well-read generalist does — broad but shallow. Domain-specific AI knows construction the way an experienced practitioner does — deep and practical.

General AI generates plausible-sounding answers about construction topics but cannot verify them against your actual project documents, cite specific code sections, or understand the relationships between drawings and specifications. When you ask ChatGPT about a building code requirement, it gives you a general answer that may or may not match the specific code edition and jurisdiction for your project. When you ask domain-specific AI the same question, it searches the applicable code document and returns the exact requirement with a section reference.

The practical test is simple: ask the AI a question where you already know the answer, and check whether it gets it right with a proper citation. If the AI gives you the correct code section number, the correct requirement, and shows you exactly where in the document it found the answer, it is grounded in actual data. If it gives you a confident but uncited answer, it is generating from training data — and you have no way to verify whether the answer is correct for your specific project and jurisdiction.