Jacob Rockowitz Details Lessons From AI-Assisted Drupal Module Development

Developer Jacob Rockowitz has published a blog post describing how AI coding tools were used to build the Drupal contributed module Entity/Field Labels, outlining both the initial progress and the failures that required manual correction. The project began with Claude Code generating a full implementation and passing unit tests, but the module failed during real-world testing due to incorrect assumptions about Drupal entity handling.

A central issue identified in the post is the limitation of AI-generated tests. Rockowitz explains that mocked unit tests allowed incorrect logic to pass, including a case where the code targeted the wrong entity definition, using node instead of node_type. Switching to kernel tests exposed these errors by forcing interaction with actual Drupal entities, leading to corrections in the implementation.

After these setbacks, Rockowitz revised his workflow. He moved away from a single large prompt to smaller, feature-specific tasks and introduced OpenAI Codex to review tests and suggest improvements. This approach supported incremental development through Drupal.org issues, including work on multilingual CSV handling, Drush integration, and improved support for base fields.

The post concludes with a broader observation about AI-assisted development in Drupal. Rockowitz emphasises that while tools such as Claude Code and Codex can accelerate prototyping and testing, developers remain responsible for understanding and reviewing the code they contribute, particularly when automated outputs appear to succeed without fully working in practice.