Contexts

Contexts in ThoughtFabric are the invisible threads that weave your conversations, documents, and ideas into a coherent knowledge network. Understanding context management is key to leveraging the full power of AI-enhanced thinking.

What is Context?

Context in ThoughtFabric refers to the background information, previous conversations, and related content that informs and enhances your current AI interactions. It's the memory that makes your conversations intelligent and connected.

Types of Context

Types of Context

• Conversation Context: The ongoing memory within a single chat thread
• Cross-Thread Context: Information shared between different threads
• Document Context: Content from uploaded files and external sources

How Context Works

Automatic Context Management

Built-in Intelligence:

• Thread Memory: Each conversation thread maintains its own context automatically
• Session Continuity: Context persists across app sessions and device restarts
• Smart Boundaries: Context is intelligently scoped to maintain relevance

Manual Context Control

Precise Context Curation:

• Add Context: Deliberately include specific content from other threads
• Remove Context: Clean up irrelevant or outdated context references
• Context Preview: See exactly what context is active in any conversation

Working with Context

Adding Context to Conversations

Step-by-Step Process

1. Open Chat Thread: Navigate to any conversation thread
2. Click "Add Context": Find the context button in the thread interface
3. Search Content: Use the search function to find relevant blocks or threads
4. Select Context: Choose specific content to include in your conversation
5. Confirm Addition: Context becomes immediately available to the AI

What You Can Add as Context

• Conversation Blocks: Specific messages or exchanges from other chats
• Document Excerpts: Relevant sections from uploaded files
• Thread Summaries: Overview content from complete threads
• URL Content: Information extracted from web pages or videos

Managing Active Context

Context Indicators

Visual Context Awareness:

• Context Tags: See which external content is active in your conversation
• Source Links: Trace context back to its original source
• Context Count: Understand the volume of context in your conversation

Context Operations

Fine-Tuned Control:

• Remove Context: Click 'X' icon or re-select to remove specific context
• Reorder Context: Arrange context priority for optimal AI understanding
• Context History: Track what context has been used in past conversations

Context Strategies

Building Knowledge Networks

Sequential Context Building

Layer Information Progressively:

1. Start Simple: Begin with basic concepts or questions
2. Add Depth: Introduce relevant context from documents or previous conversations
3. Build Complexity: Layer additional context to explore sophisticated topics
4. Maintain Focus: Remove outdated context to keep conversations sharp

Cross-Pollination

Connect Different Areas:

• Multi-Domain Thinking: Combine context from different subject areas
• Comparative Analysis: Use context to compare different approaches or sources
• Synthesis: Create new insights by connecting previously separate ideas

Context Best Practices

Advanced Context Techniques

Context Inheritance

Automatic Context Flow:

• Thread Relationships: Connected threads can share relevant context automatically
• Document Integration: File uploads automatically provide context to related conversations
• URL Context: Web content becomes available context for relevant discussions

Context Persistence

Long-Term Context Management:

• Session Memory: Context survives app restarts and device changes
• Context Libraries: Build reusable context collections for recurring topics
• Context Templates: Create standard context setups for specific types of work

Context Optimization

Performance Considerations:

• Context Limits: Understand the boundaries of how much context can be effectively used
• Context Quality: Focus on high-quality, relevant context rather than volume
• Context Refresh: Periodically review and update long-standing context

Context in Different Thread Types

Chat Thread Context

Conversational Memory:

• Natural Flow: Context builds naturally through conversation
• External Integration: Add context from documents, URLs, and other threads
• Context Branching: Explore different directions while maintaining core context

Document Thread Context

Document-Centric Context:

• Source Context: The document itself provides primary context
• Related Documents: Connect multiple documents through shared context
• Annotation Context: Your notes and highlights become part of the context

URL Thread Context

Web Content Integration:

• Page Context: Website content becomes searchable and referenceable
• Video Context: YouTube transcripts provide detailed context for discussions
• External References: Web content can contextualize conversations about external topics

Troubleshooting Context

Common Context Issues

Context Overload

• Symptoms: AI responses become unfocused or overly complex
• Solution: Remove non-essential context and focus on core relevant information

Missing Context

• Symptoms: AI seems to "forget" important information
• Solution: Explicitly add relevant context from previous conversations or sources

Outdated Context

• Symptoms: AI references old or irrelevant information
• Solution: Review and refresh context regularly, removing outdated references

Context Optimization Tips

Regular Maintenance:

• Context Audit: Periodically review active context in important conversations
• Context Cleanup: Remove irrelevant or outdated context references
• Context Updates: Refresh context when new information becomes available

Integration with AI Models

Model-Specific Context Handling

Understanding Model Differences:

• Context Windows: Different AI models have varying context capacity
• Context Processing: Models process context differently based on their training
• Optimization: Some models work better with specific types of context organization

Context and Model Performance

Maximizing AI Effectiveness:

• Context Quality: High-quality context leads to better AI responses
• Context Structure: Well-organized context helps AI understand relationships
• Context Relevance: Focused context produces more accurate and useful responses

Next Steps

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