The most effective engineering prompts follow a three-part structure: context, deliverable, and scope. Context means specifying the application, materials, loads, constraints, and operating environment — not just the topic. Deliverable means telling the model exactly what format you want back, whether that is a bullet list, a comparison table, or a JSON object. Scope means setting boundaries such as "limit to top five risks" or "focus on passive cooling only." Without all three, ChatGPT defaults to generic answers that require more time to fix than they save. Advanced techniques like role-playing ("Act as a senior mechanical engineer reviewing a pressure vessel design") and chain-of-thought prompting ("First list thermal loads, then suggest cooling methods, then compare in a table") force the model into specialist reasoning rather than surface-level responses.