Context Switching
Problem
Users frequently switch between different tasks, topics, or projects when working with AI systems, but lose context and have to repeat information each time they switch. This creates friction and reduces productivity.
Solution
Implement intelligent context management that tracks multiple conversation threads, remembers relevant information for each context, and provides seamless transitions between different topics while maintaining continuity within each context.
Examples in the Wild
Interactive Code Example
Implementation & Considerations
Implementation Guidelines
Maintain conversation history across sessions with clear visual indicators of context boundaries
Allow users to explicitly save and name different contexts for easy switching
Provide visual cues when context changes to avoid confusion about what the AI remembers
Implement smart context summarization to avoid overwhelming users with full history
Enable users to merge or split contexts when tasks overlap or diverge
Store context preferences locally first, syncing to cloud only with user permission
Design Considerations
Privacy concerns when storing conversation history across multiple contexts
Memory and storage limitations when maintaining extensive context across sessions
Potential for context confusion when switching rapidly between similar tasks
Need to balance context retention with the ability to start fresh conversations
Computational cost of maintaining and retrieving multiple active contexts
Risk of surfacing outdated or irrelevant information from previous contexts