Continuous AI Workflow for Reviewing Dictionary (Word) Contributions

[babblebey]

Implementation

Summary

Introduce a Continuous AI (CAI) workflow that automatically reviews word contributions made to the dictionary. Contributions submitted via the jargons-editor are already tagged with labels like 📖new-word, 📖edit-word, and 💻via-jargons-editor. The workflow will leverage these labels to identify relevant pull requests and automatically perform an AI-assisted review.

Motivation

Consistency and clarity are essential for building a developer-friendly dictionary. Currently, human reviewers ensure that contributions follow our desired structure meaning → explanation → example (if necessary), but this adds friction and may discourage contributors due to slower feedback loops. Automating this first-pass review ensures contributors get instant, constructive guidance while maintainers focus on higher-level decisions.

By using GitHub Models, GenAIScript, or other AI technologies, we can integrate this process directly into GitHub Actions, embedding AI review directly into our contribution workflow.

Goal

• Automatically trigger AI review on pull requests with labels 📖new-word or 📖edit-word (and 💻via-jargons-editor).
• Validate whether the contribution follows the expected structure:
  • Meaning (concise definition)
  • Explanation (clear, contextual elaboration)
  • Example (optional, if applicable)
• Provide actionable suggestions or improvements as review comments.
• Ensure feedback feels constructive and educational, not blocking.
• Allow maintainers to accept, refine, or dismiss AI suggestions before merging.