Current chat interfaces for large language models (LLMs) often feel restrictive, especially for users handling complex tasks. They struggle with organizing conversations, lack rich media support, and don't integrate well with other tools. This makes them less useful for professional workflows, collaborative work, or long-term knowledge management.

Rethinking the Chat Interface

One way to improve LLM chat interfaces could be to transform them into flexible workspaces. Instead of a single linear chat, users might benefit from features like:

• Conversation threading: Splitting discussions into sub-topics (e.g., research notes separate from draft edits)
• Multimodal inputs: Dragging and dropping images or documents directly into the chat, with tools to annotate them
• Custom layouts: Resizable panels, pinned messages, or multiple simultaneous chats
• Tool integrations: Connecting to calendars, task managers, or note-taking apps without leaving the chat

These enhancements could make the interface more useful for researchers, writers, and teams working on complex projects. Voice commands and gesture controls might also improve accessibility.

Implementation Strategy

A practical approach might start with a browser extension adding basic threading and pinning features to existing LLM chats. Subsequent phases could introduce:

1. Image/PDF support with simple annotation tools
2. Integration with common productivity apps
3. A standalone application with advanced features like voice control

Early versions might work with open-source models to avoid platform restrictions, while later iterations could partner with commercial LLM providers.

Balancing Features and Usability

While adding functionality, it would be important to avoid overwhelming users. Features could be modular - allowing users to enable only what they need. The interface might borrow familiar elements from productivity tools while maintaining the immediacy of chat. For example, threads could be collapsible and visually distinct to maintain clarity.

This approach could create a more versatile interface that bridges the gap between simple chat and full productivity suites, adapting to different workflows without requiring changes to the underlying AI models.