Six questions to ask when evaluating AI CAD tools
From inputs and outputs to validation and delivery: build an evaluation that an engineering team can actually run.
When trying a new tool, a team needs to turn a demonstration into its own acceptance tasks. These six questions help define an evaluation with explicit inputs and deliverables.
1. Are the inputs clear enough?
Prepare a real specification covering dimensions, units, materials, interfaces and reference models. Record how missing information is clarified and which assumptions the system makes.
2. What is the output?
Establish whether the deliverable is an image, mesh, solid, modeling script or native model with history. Then open and inspect it in the team’s everyday software.
3. Can it still be edited?
Choose representative design changes. Check dimension edits, feature rebuilds and assembly references. Save the failure cases.
4. How are results validated?
Check geometric dimensions, model structure and engineering requirements separately. Record what automated checks cover and what engineers still review.
5. How does a failed task recover?
Look at interruptions, retries, version differences and invalid input. Keep the full process, not just a screenshot of the final success.
6. Does it fit the existing workflow?
Consider file exchange, permissions, team reuse and running costs. Repeat the evaluation with a fixed set of tasks to build the team’s own evidence.
This is an editorial evaluation method, not a product ranking or purchasing conclusion.
