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Context Window Management UI(CWM)
Visual patterns for managing LLM context limits, token usage, and context window optimization in agent interfaces
Complexity: mediumUI/UX & Human-AI Interaction
🎯 30-Second Overview
Pattern: Visual interfaces for managing LLM token limits and conversation context
Why: Context windows have hard limits that can break conversations and waste tokens without proper management
Key Insight: Show token usage in real-time + let users prioritize context + auto-compress when needed
⚡ Quick Implementation
1Token Tracking:Real-time counting of input/output tokens
2Usage Meters:Visual progress bars showing context consumption
3Priority Controls:Pin important messages, compress or remove others
4Smart Compression:Summarize old conversations while preserving key info
5Auto-Management:Proactive suggestions when approaching limits
Example: Token meter (75% full) → Warning → Pin key messages → Compress old → Continue conversation
📋 Do's & Don'ts
✅Show real-time token usage with clear visual indicators
✅Allow users to pin critical messages and context
✅Provide reversible compression and smart summarization
✅Warn users before approaching token limits
✅Implement sliding window management for long conversations
❌Let conversations fail without warning
❌Remove context without user awareness or control
❌Hide token consumption and usage patterns
❌Lose critical context during compression
❌Make context management too complex for users
🚦 When to Use
Use When
- • Long conversations and sessions
- • Document analysis with large context
- • Multi-turn reasoning tasks
- • Cost-sensitive applications
Avoid When
- • Single-turn question answering
- • Short conversational interactions
- • Stateless API calls
- • Simple tool usage scenarios
📊 Key Metrics
Context Efficiency
% of useful tokens vs total consumed
Compression Ratio
Token reduction from smart compression
User Satisfaction
Preference for auto vs manual context management
Conversation Length
Extended interactions enabled by management
Error Prevention
Reduction in context overflow failures
Cost Optimization
Token cost savings from efficient management
💡 Top Use Cases
Document Analysis: Manage large PDF context while maintaining conversation history
Code Review: Pin critical code sections, compress older discussion threads
Research Tasks: Retain key findings while processing multiple source documents
Customer Support: Maintain conversation context while accessing knowledge base
Creative Writing: Preserve character/plot details while developing long narratives
References & Further Reading
Deepen your understanding with these curated resources
Contribute to this collection
Know a great resource? Submit a pull request to add it.
🪟
Context Window Management UI(CWM)
Visual patterns for managing LLM context limits, token usage, and context window optimization in agent interfaces
Complexity: mediumUI/UX & Human-AI Interaction
🎯 30-Second Overview
Pattern: Visual interfaces for managing LLM token limits and conversation context
Why: Context windows have hard limits that can break conversations and waste tokens without proper management
Key Insight: Show token usage in real-time + let users prioritize context + auto-compress when needed
⚡ Quick Implementation
1Token Tracking:Real-time counting of input/output tokens
2Usage Meters:Visual progress bars showing context consumption
3Priority Controls:Pin important messages, compress or remove others
4Smart Compression:Summarize old conversations while preserving key info
5Auto-Management:Proactive suggestions when approaching limits
Example: Token meter (75% full) → Warning → Pin key messages → Compress old → Continue conversation
📋 Do's & Don'ts
✅Show real-time token usage with clear visual indicators
✅Allow users to pin critical messages and context
✅Provide reversible compression and smart summarization
✅Warn users before approaching token limits
✅Implement sliding window management for long conversations
❌Let conversations fail without warning
❌Remove context without user awareness or control
❌Hide token consumption and usage patterns
❌Lose critical context during compression
❌Make context management too complex for users
🚦 When to Use
Use When
- • Long conversations and sessions
- • Document analysis with large context
- • Multi-turn reasoning tasks
- • Cost-sensitive applications
Avoid When
- • Single-turn question answering
- • Short conversational interactions
- • Stateless API calls
- • Simple tool usage scenarios
📊 Key Metrics
Context Efficiency
% of useful tokens vs total consumed
Compression Ratio
Token reduction from smart compression
User Satisfaction
Preference for auto vs manual context management
Conversation Length
Extended interactions enabled by management
Error Prevention
Reduction in context overflow failures
Cost Optimization
Token cost savings from efficient management
💡 Top Use Cases
Document Analysis: Manage large PDF context while maintaining conversation history
Code Review: Pin critical code sections, compress older discussion threads
Research Tasks: Retain key findings while processing multiple source documents
Customer Support: Maintain conversation context while accessing knowledge base
Creative Writing: Preserve character/plot details while developing long narratives
References & Further Reading
Deepen your understanding with these curated resources
Contribute to this collection
Know a great resource? Submit a pull request to add it.