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I'm not sure if this is the right project for this idea, but am I the only one who wants the ability to manage the content of n_keep so that you can better manage context over long conversations and prevent that rabbit trail you went down three prompts ago doesn't continue to corrupt your model's output? A more detailed problem statement can be found below:
Problem Statement:
LLM users lack tools to manage their context window (n_keep) before hitting token limits, leading to disruptive conversation restarts.
Objective:
Develop a context management system enabling users to monitor, tag, edit, and optimize their context window content before submission to LLMs.
Scope:
Context utilization monitoring and alerts
Content tagging/editing interface
Version control and history tracking
Context visualization tools
Integration with existing LLM platforms
Expected Outcomes:
Reduced conversation disruptions
Improved context relevance through user management
Enhanced conversation flow
Increased user satisfaction through proactive context control
Success Metrics:
Reduction in forced conversation restarts
Context window utilization efficiency
User engagement with management tools
Maintained response coherence post-editing
This solution addresses the critical need for user control over context management in LLM interactions.
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I'm not sure if this is the right project for this idea, but am I the only one who wants the ability to manage the content of n_keep so that you can better manage context over long conversations and prevent that rabbit trail you went down three prompts ago doesn't continue to corrupt your model's output? A more detailed problem statement can be found below:
Problem Statement:
LLM users lack tools to manage their context window (n_keep) before hitting token limits, leading to disruptive conversation restarts.
Objective:
Develop a context management system enabling users to monitor, tag, edit, and optimize their context window content before submission to LLMs.
Scope:
Context utilization monitoring and alerts
Content tagging/editing interface
Version control and history tracking
Context visualization tools
Integration with existing LLM platforms
Expected Outcomes:
Reduced conversation disruptions
Improved context relevance through user management
Enhanced conversation flow
Increased user satisfaction through proactive context control
Success Metrics:
Reduction in forced conversation restarts
Context window utilization efficiency
User engagement with management tools
Maintained response coherence post-editing
This solution addresses the critical need for user control over context management in LLM interactions.
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