How-to
Note Taking
Collaborative note-taking where AI and humans work together to capture, enhance, and connect information in real-time
Basic Memory transforms note-taking into a collaborative process where both you and AI can read, write, and enhance notes together. The semantic knowledge graph means every note becomes part of a connected web of understanding that grows smarter over time.
The Two-Way Knowledge Flow
Human Captures, AI Enhances
You take quick notes during a meeting:
# Team Meeting - Project Alpha
- Sarah mentioned database issues
- Need to update API docs
- Budget concerns raised by finance
- Next milestone is March 15th
AI reads your note and enhances it:
You: "Clean up and expand my meeting notes, connecting to our existing project knowledge"
AI: [Reads your raw notes and creates:]
- Structured semantic observations with tags
- Connections to existing project documentation
- Action items with clear ownership
- Links to related technical issues and timelines
- Enhanced context from previous meeting notes
AI Creates, Human Refines
AI generates comprehensive notes:
You: "Create detailed notes from this lecture recording about machine learning"
AI: [Creates structured notes with:]
- Key concepts with semantic tags
- Technical details and explanations
- Connections to existing AI knowledge
- Questions for further exploration
You read and add personal insights:
# Machine Learning Fundamentals - Lecture Notes
[AI-generated technical content...]
## My Thoughts and Questions
- [insight] This connects to what I learned about neural networks last month #personal-connection
- [question] How does this apply to the project I'm working on? #application
- [confusion] Need to understand the math behind gradient descent better #learning-gap
## Relations
- builds_on [[Neural Networks Basics]]
- applies_to [[Current Work Project]]
Real-Time Collaborative Workflows
Meeting Notes
During the meeting (human captures quickly):
# Marketing Strategy Meeting - Q1 Planning
## Attendees
Sarah (Marketing), Mike (Product), Jenny (Sales)
## Key Points
- Q4 conversion rates down 15%
- New competitor launched similar product
- Need to revise messaging strategy
- Budget discussions - Sarah wants $50k for ads
- Mike concerned about feature parity
After the meeting (AI enhances and connects):
You: "Enhance these meeting notes and connect them to our existing marketing and product knowledge"
AI: [Transforms into structured knowledge:]
- Semantic observations with business impact tags
- Connections to previous quarter performance data
- Links to competitor analysis notes
- Action items with deadlines and owners
- Relations to product roadmap and marketing strategy
Result - enhanced collaborative note:
---
title: Marketing Strategy Meeting - Q1 Planning
tags: [marketing, strategy, q1-planning, performance-review]
---
# Marketing Strategy Meeting - Q1 Planning
## Meeting Context
- [date] January 15, 2024 #timeline
- [attendees] Sarah (Marketing), Mike (Product), Jenny (Sales) #participants
- [purpose] Q1 strategy planning and Q4 performance review #meeting-type
## Performance Analysis
- [metric] Q4 conversion rates decreased 15% year-over-year #performance-decline
- [context] Market pressure from new competitor launch #competitive-pressure
- [impact] Revenue targets missed by $200k in Q4 #financial-impact
## Strategic Discussions
- [proposal] Messaging strategy revision to emphasize unique value props #strategy-shift
- [request] $50k additional advertising budget for Q1 #budget-request
- [concern] Product feature parity gap with new competitor #product-gap
- [priority] Need coordinated marketing-product response #cross-team-coordination
## Action Items
- [ ] Sarah: Draft new messaging framework by Jan 22 #action-item
- [ ] Mike: Assess feature gap and provide development timeline #action-item
- [ ] Jenny: Analyze Q4 sales cycle data for conversion insights #action-item
- [ ] All: Follow-up meeting scheduled for Jan 29 #next-steps
## Relations
- follows_up [[Q4 Performance Review]]
- addresses [[Competitor Analysis - New Market Entrant]]
- informs [[Q1 Marketing Strategy]]
- affects [[Product Roadmap Q1]]
- requires [[Budget Planning Q1]]
Lecture and Learning Notes
During lecture (human jots down key points):
# Quantum Computing Lecture - Entanglement
Prof. Martinez - Physics 451
- Quantum entanglement = spooky action at distance
- Bell's theorem proves local realism is wrong
- EPR paradox - Einstein didn't like this
- Applications in quantum teleportation
- Measurement collapses entangled state
After lecture (AI expands and connects):
You: "Expand these lecture notes with detailed explanations and connect to my existing physics knowledge"
AI: [Creates comprehensive note with:]
- Detailed explanations of each concept
- Mathematical foundations where relevant
- Historical context and key experiments
- Connections to previous quantum mechanics notes
- Questions for further study
- Relations to quantum computing applications
Book and Article Notes
While reading (human highlights key insights):
# Notes from "Thinking, Fast and Slow" - Chapter 3
## Key Ideas
- System 1 vs System 2 thinking
- Cognitive ease affects judgment
- Availability heuristic leads to biases
- Examples: lottery numbers, recent events
AI enhances with connections:
You: "Expand these book notes and connect them to my existing knowledge about psychology and decision-making"
AI: [Enhances with:]
- Detailed explanations of each concept
- Real-world examples and applications
- Connections to behavioral economics notes
- Links to personal decision-making experiences
- Questions about practical applications
The Semantic Knowledge Graph Advantage
Automatic Context Building
When you or AI create notes, the semantic structure automatically builds context:
# Today's Client Meeting - Website Redesign
## Project Requirements
- [requirement] Mobile-first design approach #web-design
- [requirement] Integration with existing CRM system #technical-integration
- [constraint] Launch deadline is April 30th #timeline
- [budget] $25k total project budget #financial
## Technical Considerations
- [technology] Client uses Salesforce CRM #crm-system
- [challenge] Legacy API has rate limiting #technical-constraint
- [solution] Need to implement caching layer #technical-solution
## Relations
- project_for [[Client ABC Corporation]]
- uses_technology [[Salesforce Integration]]
- deadline_affects [[Q2 Revenue Projections]]
- requires [[Frontend Development Skills]]
AI can now automatically connect this to:
- Previous client projects and lessons learned
- Technical documentation about Salesforce APIs
- Team capacity and skill assessments
- Budget tracking and project profitability
Search-Driven Context Loading
The semantic structure enables powerful search and context loading:
You: "Load context about all our Salesforce integration projects"
AI: [Searches semantic graph and finds:]
- Technical challenges from previous integrations
- Code patterns and solutions that worked
- Client requirements and common requests
- Budget and timeline patterns
- Team expertise and resource needs
Cross-Domain Connections
The knowledge graph automatically suggests unexpected connections:
You: "I'm taking notes on urban planning principles"
AI: [While creating the note, suggests connections to:]
- Software architecture patterns (similar design principles)
- Psychology notes about human behavior in spaces
- Economics research on local development
- Environmental studies about sustainable cities
Different Note Types and Workflows
Quick Capture Notes
For immediate idea capture:
# Ideas - Mobile App Feature
## Random Thoughts
- Push notifications for habit tracking
- Gamification with points/badges
- Social sharing of achievements
- Integration with calendar apps
- Offline mode for data entry
[AI later enhances with feasibility analysis, technical requirements, and market research connections]
Voice-to-Text Processing
After voice recording transcription:
You: "I recorded my thoughts during my commute. Clean up this transcript and turn it into structured notes"
AI: [Processes voice transcript into:]
- Cleaned up text with proper punctuation
- Organized thoughts by topic
- Semantic observations with tags
- Connections to existing projects and ideas
- Action items extracted from rambling thoughts
Progressive Note Building
Note evolves through multiple AI-human iterations:
Day 1 - Human starts:
# Project Planning - New E-commerce Site
Need to plan the new e-commerce site for Q2 launch
Day 2 - AI adds structure:
# Project Planning - New E-commerce Site
## Timeline
- [milestone] Q2 launch target #timeline
- [phase] Discovery and planning - January #project-phase
- [phase] Design and development - Feb-March #project-phase
- [phase] Testing and launch - April #project-phase
Day 3 - Human adds requirements:
[Previous content...]
## Requirements Gathered
- Mobile-responsive design essential
- Payment processing via Stripe
- Inventory management integration
- Customer account portal
Day 4 - AI connects to existing knowledge:
[Previous content...]
## Relations
- similar_to [[Previous E-commerce Project]]
- requires [[Stripe Integration Knowledge]]
- uses [[React Frontend Skills]]
- impacts [[Q2 Revenue Goals]]
Advanced Collaborative Patterns
Note Handoffs
Human starts research, AI continues:
You: "I started researching renewable energy storage. Continue this research and create detailed technical notes."
AI: [Reads your initial notes and:]
- Expands on battery technologies
- Adds grid-scale storage solutions
- Connects to energy policy research
- Identifies key research papers and companies
- Creates comprehensive technical overview
AI drafts, human personalizes:
You: "You created great notes on meditation techniques. Add my personal experiences and what works for me."
[Human adds personal insights, preferred methods, and specific outcomes to AI's comprehensive overview]
Iterative Enhancement
Multiple rounds of AI-human collaboration:
Round 1: Human captures raw meeting notes
Round 2: AI structures and enhances with context
Round 3: Human adds personal insights and reactions
Round 4: AI connects to broader strategic implications
Round 5: Human adds action items and next steps
Context-Aware Note Creation
AI uses full knowledge graph context:
You: "Create notes for the team retrospective meeting"
AI: [Creates template based on:]
- Previous retrospective formats and questions
- Current project status and challenges
- Team dynamics and recent feedback
- Goals and metrics being tracked
- Suggested improvements from past retros
Best Practices for Collaborative Note-Taking
Human Best Practices
- Capture quickly - Don't worry about structure initially
- Use consistent language - Helps AI understand and connect concepts
- Add personal insights - Your unique perspective enhances AI content
- Review AI enhancements - Verify and refine AI additions
- Create relations explicitly - Guide the knowledge graph development
AI Enhancement Patterns
- Structure unstructured input - Convert rambling notes to organized content
- Add semantic tags - Enable search and connection capabilities
- Connect to existing knowledge - Link new notes to relevant existing content
- Expand with context - Add background information and explanations
- Suggest next steps - Identify follow-up actions and questions
Collaborative Workflows
- Real-time handoffs - Pass notes back and forth during active work
- Scheduled enhancements - Regular AI processing of accumulated notes
- Context integration - Use search to load relevant background before note creation
- Progressive building - Build complex notes through multiple iterations
- Cross-reference checking - Verify consistency across related notes
Technical Note-Taking Scenarios
Code Review Notes
Human captures initial thoughts:
# Code Review - User Authentication Module
## Issues Found
- Password validation too weak
- No rate limiting on login attempts
- SQL injection vulnerability in user lookup
- Missing input sanitization
AI enhances with technical details:
AI adds:
- Specific code line references
- Security vulnerability classifications
- Connections to security best practices notes
- Similar issues found in previous reviews
- Recommended fixes with code examples
Conference and Workshop Notes
During conference (human captures key points):
# DevCon 2024 - Day 1 Notes
## Keynote - Future of Web Development
Speaker: Sarah Chen
- Web Assembly becoming mainstream
- JAMstack architecture patterns
- Edge computing changing everything
AI expands with comprehensive coverage:
AI enhances with:
- Detailed explanations of technical concepts
- Connections to existing web development knowledge
- Links to speaker's previous work and papers
- Integration with current project implications
- Questions for further research
Research Paper Notes
Human extracts key insights:
# Paper Notes - "Attention Is All You Need"
## Main Contribution
- Transformer architecture replaces RNNs
- Self-attention mechanism
- Parallelizable training
- Better performance on translation tasks
AI creates comprehensive analysis:
AI adds:
- Mathematical foundations of attention
- Comparison with previous sequence models
- Impact on subsequent research and applications
- Connections to other AI architecture notes
- Implementation considerations and code examples
Troubleshooting Collaborative Note-Taking
Common Challenges
AI enhancements don't match my thinking style
Solutions:
- Provide examples of your preferred note structure
- Add explicit instructions about your preferences
- Review and edit AI enhancements to train patterns
- Use tags consistently to help AI understand your categories
Notes becoming too long and overwhelming
Solutions:
- Use hierarchical structure with clear sections
- Create summary notes that link to detailed content
- Use semantic search to find specific information quickly
- Archive old notes while keeping important connections
Losing track of which content is human vs AI
Solutions:
- Use consistent patterns for personal insights
- Add metadata about content sources
- Create separate sections for AI enhancements
- Review and approve all AI additions before finalizing
Integration with Daily Workflows
Meeting Preparation
Before the meeting:
You: "Load context for tomorrow's project planning meeting"
AI: [Searches knowledge graph and provides:]
- Previous meeting notes and action items
- Current project status and blockers
- Team member updates and capacity
- Related decisions and requirements
- Suggested agenda items based on outstanding issues
Daily Reflection
End of day review:
You: "Review today's notes and create a summary of key insights and next steps"
AI: [Analyzes day's notes and creates:]
- Summary of main themes and decisions
- Outstanding questions and action items
- Connections to broader goals and projects
- Suggestions for tomorrow's priorities
Weekly Planning
Weekly review process:
You: "Analyze this week's notes and identify patterns, progress, and planning needs"
AI: [Reviews week's knowledge creation and provides:]
- Progress tracking against goals
- Emerging themes and insights
- Knowledge gaps requiring attention
- Connections between different work streams
- Strategic implications and recommendations
Next Steps
Research & Learning
Build comprehensive knowledge bases where AI creates detailed, interconnected notes that grow your understanding over time
Personal Knowledge Management
Build a comprehensive life management system that connects your goals, experiences, relationships, and growth across all areas of life