#atom

Subtitle:

AI-generated content tailored to personal knowledge contexts

Core Idea:

Context-aware responses are AI-generated answers that incorporate and reference a user's existing knowledge base, creating personalized responses that connect to, cite, and build upon the specific information in their notes or documents.

Key Principles:

1. Retrieval-Augmented Generation:
   • Combines information retrieval with text generation
   • Uses relevant documents from personal knowledge as context for responses
2. Personal Knowledge Grounding:
   • Anchors responses in the user's own information and terminology
   • Prioritizes personal notes over general knowledge when appropriate
3. Relevance Filtering:
   • Selectively includes only the most pertinent context for each query
   • Balances comprehensiveness with precision in context selection

Why It Matters:

• Personalized Knowledge Access:
  • Turns static notes into interactive, queryable knowledge
  • Provides answers in the context of personal understanding and priorities
• Cognitive Extension:
  • Surfaces forgotten or overlooked information when needed
  • Connects isolated pieces of knowledge into coherent responses
• Trust and Verification:
  • Shows sources from personal knowledge base for verification
  • Maintains traceability between responses and underlying information

How to Implement:

1. Context Retrieval:
   • Use semantic search to find relevant documents for the query
   • Select and rank context based on relevance to the specific question
2. Context Integration:
   • Format retrieved context to include in the prompt
   • Specify how the AI should use and cite the provided context
3. Response Generation:
   • Generate answer that synthesizes the retrieved information
   • Include references to specific notes or sections used

Example:

• Scenario:

  • User asks: "What are the key arguments for implementing carbon pricing?"

• Application:

    // 1. Retrieve relevant context
    const relevantNotes = semanticSearch("carbon pricing arguments benefits implementation", topK=5);
    
    // 2. Format prompt with context
    const prompt = `
    Based on the following notes from my knowledge base:
    
    ${relevantNotes.map(note => `From [[${note.title}]]: ${note.content}`).join('\n\n')}
    
    Please answer: What are the key arguments for implementing carbon pricing?
    Reference specific notes when providing information.
    `;
    
    // 3. Generate response using language model
    const response = await languageModel.generate(prompt);

• Result:

  • User receives a response that synthesizes their existing notes on carbon pricing
  • Response includes specific references to their notes for verification
  • Information is presented in a way that aligns with their existing knowledge

Connections:

• Related Concepts:
  • Smart Chat: Implementation of context-aware responses in note-taking apps
  • HyDE Retrieval Strategy: Method for improving context selection for responses
• Broader Concepts:
  • RAG (Retrieval Augmented Generation): Technical framework combining retrieval with generation
  • AI-Enhanced Note Taking: Category of tools using AI to improve knowledge work

References:

1. Primary Source:
   • "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" (Lewis et al., 2020)
2. Additional Resources:
   • Smart Connections Plugin documentation on Smart Chat
   • "In-Context Learning and Inferences from Context" (OpenAI research)

Tags:

#AI #context-awareness #personalization #RAG #knowledge-management #chatbots

Connections:

Sources:

• From: Obsidian Plugin Smart Connections