https://ezremove.ai/ai-video-generator/The promise sounds almost too convenient: describe a part in plain English and receive a production-ready STEP or STL file a minute later. The combination of LLMs, CadQuery, OpenSCAD, and cloud-based CAD platforms such as Zoo.dev has made text-to-CAD accessible not only to research labs, but also to everyday engineers. Zoo, for example, can generate editable parametric models from text prompts.

We tested several AI CAD generation tools on typical mechanical parts, ranging from simple brackets to objects with multiple bodies, holes, fillets, and interdependent dimensions. The results were more interesting than the usual debate about whether AI will replace engineers. The technology is already useful — sometimes impressively so — but it reaches its limits much sooner than product demos suggest.

What “Text-to-CAD” Actually Promises

In its most common form, a text-to-CAD system receives a written prompt, converts it into CadQuery or OpenSCAD code, executes that code, and exports the resulting geometry as a STEP, STL, or another CAD-compatible format. More closed platforms may generate models directly, but the underlying idea remains the same: the user describes the geometry, and the model creates a sequence of operations instead of requiring manual work in a CAD interface.

This is not hype without substance. In one study, researchers expanded a dataset with approximately 170,000 CadQuery annotations and fine-tuned six open-source LLMs. The best-performing model achieved a 69.3% top-1 exact-match accuracy, compared with a baseline result of 58.8%.

There is, however, an important qualification. That result was not achieved because any general-purpose chatbot can suddenly design mechanical parts. It depended on LLM fine-tuning using specialized CAD data. In other words, CadQuery AI and OpenSCAD AI systems perform best when the model understands a specific construction language, common modeling operations, and the structure of parametric geometry.

For companies that need a model trained on their own parts library, naming conventions, and internal engineering standards, this becomes a large language model development problem rather than a matter of finding a better prompt. The quality of AI generated CAD models depends on training data and model customization at least as much as it depends on model size.

Where It Actually Works

We obtained the best results with parts that could be expressed as a short sequence of predictable operations: create a sketch, extrude a profile, add holes, and repeat a feature using a pattern. Bolts, spacers, mounting plates, simple enclosures, and L-shaped brackets are almost ideal for this approach.

In one test, we asked a system to create a mounting plate with four holes, a central cutout, and configurable values for width, thickness, and hole spacing. The first version could already be exported, and after one follow-up prompt, the model converted the main dimensions into variables. This is more than a toy demonstration. For a family of similar parts, a script like this could save a meaningful amount of engineering time.

Rapid prototypes also work well when obtaining the overall shape matters more than producing flawless manufacturing geometry. For a visual mockup of a sensor enclosure or an early assembly clearance check, it may be enough to have an LLM generate a CAD model with approximately the right dimensions and let an engineer correct the details afterward.

This is why engineering feedback around code-based CAD is often positive. Parametric automation is especially useful for catalogs of similar components where dimensions are the main variables. One CadQuery user, for example, described automatically generating approximately a thousand electronic components that differed primarily in their parameters. In these scenarios, AI is not inventing geometry. It is accelerating the implementation of logic that is already understood.

Where It Breaks Down

Problems begin when a shape can no longer be represented as a sequence of obvious operations. Complex transitions, lofted surfaces, interdependent fillets, thin-walled features, and multi-body models quickly turn a prompt into a lengthy specification that may be harder to write than the part would be to model manually.

Fillets and chamfers are particularly revealing. A model may generate syntactically valid code that fails at runtime because it selects the wrong edges. It may also produce geometry that is technically valid but differs from what the user intended. Assemblies are even more challenging: a system may understand the individual components while losing track of mates, dependency order, and the engineering purpose of the constraints.

The next major boundary is GD&T, fits, and tolerances. Most text-to-CAD tools focus on the shape of the solid rather than the complete set of manufacturing information. A model may create an 8 mm hole, but that does not mean it can correctly define a positional tolerance, datum surfaces, surface finish, or inspection requirements.

AI CAD limitations become even more apparent in an actual production workflow. A model must be stored in a PDM or PLM system, receive a part number and revision, include material properties, connect to a bill of materials, and retain an approval history. A tool that generates an isolated STEP file usually knows nothing about these requirements.

Parametric CAD automation is therefore valuable as a copilot for repetitive work, but it is not yet a substitute for the engineering process. It can accelerate model creation, but it does not take responsibility for design intent, manufacturability, or data management.

When AI Hits Its Ceiling, Real CAD Engineering Takes Over

Once a task extends beyond generating a single part, the focus shifts from geometry creation to process integration. Batch processing drawings, automatically populating attributes, synchronizing CAD data with ERP or PLM systems, enforcing corporate standards, and adding custom commands to AutoCAD or SolidWorks all require access to official APIs and SDKs.

At this point, asking an LLM to “build a plugin” is not enough. The software must handle errors, manage document versions, work reliably with large assemblies, control transactions, and account for the constraints of a particular CAD kernel. ObjectARX, the SolidWorks API, and other platform-specific interfaces provide this level of access, but using them effectively requires conventional software engineering.

This is the class of problems addressed by teams working on custom CAD software development: custom CAD plugin development, CAD API integration, and production workflow automation. AI may help write part of the code or produce an early command prototype, but the reliability of the finished system still depends on architecture, testing, and detailed knowledge of the CAD platform.

Closing Thoughts

Text-to-CAD will probably become a standard step in the CAD pipeline: a quick draft, a parametric starting point, or an automated repetitive operation that an engineer then reviews and refines. It is already a useful tool, but it is not an autonomous designer. The most realistic future is not “text instead of engineers,” but a shorter path from engineering intent to the first workable geometry.

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