AI-Assisted Coding Used to Build Drupal Document Summarizer Tooltip Prototype

The module, named Document Summarizer Tooltip, detects document links on a page and displays an automatically generated summary when a user hovers over the link. The prototype is available publicly on GitHub and can be installed and tested in Drupal environments.

Minnur said the objective was to validate the concept quickly rather than hand-writing every component. Initial development was attempted using Cline with Claude Sonnet, which generated a full Drupal module structure but did not function correctly. JavaScript issues related to tooltip positioning and behaviour required further debugging, prompting a shift to Claude Code for iterative refinement.

Among the main technical challenges were incorrect tooltip positioning and missing title rendering in the tooltip header. Minnur identified the generated function responsible for the header output, introduced the required variables manually, and then adjusted the implementation with AI assistance to ensure correct rendering. Subsequent revisions improved the stability and consistency of the tooltip behaviour.

The first iteration focused exclusively on PDF files to validate the approach. After achieving stable behaviour, the module was refactored to support additional file types and renamed to reflect its broader scope. Some files retained legacy naming references and required manual correction, underscoring the need for human review when applying wide-scale AI-driven refactoring across Drupal files and configuration.

Accessibility considerations were introduced after the core functionality was established. AI tools were used to suggest ARIA attributes and interaction patterns aimed at improving keyboard and screen reader compatibility. The initial configuration form generated by the AI included more fields than necessary and was later simplified to improve clarity and usability.

The prototype also incorporated Drupal AI integration and CSRF token handling with minimal modification. However, Minnur noted that some generated Drupal API calls appeared valid but were not actually supported, requiring manual verification and correction. Automated tests were not included in this prototype, though Minnur indicated interest in exploring AI-assisted test generation in future iterations.

The project forms part of Tag1’s AI Applied content series, which documents internal experimentation with artificial intelligence tools prior to broader client implementation.