Recent advances in large language models (LLMs) have created new opportunities for automating computeraided design (CAD) workflows through natural language interactions. However, generating reliable CAD scripts remains challenging due to execution errors, geometric inconsistencies, and increasing design complexity. This paper presents an iterative AI-assisted CAD generation workflow that combines prompt engineering, CAD validation, and corrective feedback to improve the reliability of AI-generated CAD scripts. The proposed workflow evaluates ChatGPT-5, Claude, and Copilot across three CAD geometries of increasing complexity using both CQ-Editor and FreeCAD environments. Generated scripts are executed and validated for geometric correctness, watertightness, STL compatibility, and manufacturability. When execution or geometry errors are detected, visual and textual feedback is used to iteratively refine the generated scripts through a structured correction process. Experimental results show that iterative feedback significantly improves CAD generation success rates across all evaluated models. Claude was the most consistent across CAD environments and shapes; ChatGPT generally needed fewer corrections for complex shapes; and Copilot showed greater difficulty, especially when it came to more complex shapes. These results highlight the strengths and weaknesses of AI-assisted CAD generations and demonstrate how iterative feedback improves CAD script reliability.