  Computer Aided Design (CAD) engineers typically do not achieve their best
prototypes in a single attempt. Instead, they iterate and refine their designs
to achieve an optimal solution through multiple revisions. This traditional
approach, though effective, is time-consuming and relies heavily on the
expertise of skilled engineers. To address these challenges, we introduce
Query2CAD, a novel framework to generate CAD designs. The framework uses a
large language model to generate executable CAD macros. Additionally, Query2CAD
refines the generation of the CAD model with the help of its self-refinement
loops. Query2CAD operates without supervised data or additional training, using
the LLM as both a generator and a refiner. The refiner leverages feedback
generated by the BLIP2 model, and to address false negatives, we have
incorporated human-in-the-loop feedback into our system. Additionally, we have
developed a dataset that encompasses most operations used in CAD model
designing and have evaluated our framework using this dataset. Our findings
reveal that when we used GPT-4 Turbo as our language model, the architecture
achieved a success rate of 53.6\% on the first attempt. With subsequent
refinements, the success rate increased by 23.1\%. In particular, the most
significant improvement in the success rate was observed with the first
iteration of the refinement. With subsequent refinements, the accuracy of the
correct designs did not improve significantly. We have open-sourced our data,
model, and code (github.com/akshay140601/Query2CAD).
