Issue: Lack of Bulk Deletion and Project Management Controls in Google AI Studio

Hello Google Developers Community,

I’m reaching out to highlight a significant UX and management challenge within Google AI Studio that is affecting many of us, especially those working on multiple Machine Learning prototypes.

Currently, there is a clear limitation regarding Project and Chat Management:

  1. Inability to Delete Conversations/Prompts efficiently: There is no bulk-delete option for past chats. For developers running dozens of tests daily, the sidebar becomes cluttered and impossible to navigate.

  2. Persistent Projects: Many users are finding it difficult to “clean up” or permanently delete projects/folders created during the testing phase, which often leads to confusion when switching between production and staging environments.

  3. Organization Issues: Without the ability to archive or delete, the workspace quickly becomes a “junk folder” of experimental prompts, making it hard to find critical work.

Why this matters for ML Engineers & Developers:

  • Workflow Efficiency: A cluttered UI slows down the prototyping phase.

  • Privacy & Security: Developers need to be able to wipe experimental data/prompts that may contain sensitive logic or API structures once they are no longer needed.

  • Resource Management: Keeping unused “zombie” projects makes it harder to manage API quotas and project-specific settings.

Proposed Solutions:

  • A Bulk Selection tool for the sidebar to delete multiple chats at once.

  • A Clear All or Archive function for temporary experimental projects.

  • Better synchronization between Google Cloud Project (GCP) deletion and its reflection in the AI Studio UI.

Is anyone else facing this? If you have found a workaround (other than manually deleting one by one, which is not scalable) or if there is a roadmap update for this, please let us know.

Let’s upvote this to get the attention of the Gemini / AI Studio product team!

#GoogleAIStudio #GeminiAPI #DeveloperExperience feedback mlops