Best for:
├── Open-ended questions: "How can I improve team velocity?"
├── Strategic discussions: "What should our Q2 priorities be?"
├── Stakeholder communication: "Draft an update for executives"
├── Problem-solving: "We're behind schedule, what are our options?"
├── Learning and explanation: "Explain why this project is at risk"
├── Creative brainstorming: "What innovative approaches could we try?"
├── Process guidance: "How should we handle scope changes?"
└── Team management: "How do I address team conflicts?"

Avoid for:
├── Simple data lookups: "How many tickets are open?" (use Analytics)
├── Precise calculations: "What's our exact velocity?" (use Analytics)
├── Real-time status: "Is the build passing?" (use Integrations)
├── Specific metrics: "What's our defect rate?" (use Analytics)

Best for:
├── Performance metrics: "What's our team velocity trend?"
├── Forecasting: "When will we complete this epic?"
├── Comparisons: "How does this sprint compare to last sprint?"
├── Trend analysis: "Is our quality improving over time?"
├── Capacity planning: "Do we have enough capacity for Q2?"
├── ROI calculations: "What's the ROI of this project?"
├── Benchmarking: "How do we compare to industry standards?"
└── Data-driven insights: "What do the numbers tell us?"

Avoid for:
├── Subjective opinions: "Should we change our process?" (use Language)
├── Creative solutions: "How can we innovate?" (use Language)
├── Emotional context: "How is the team feeling?" (use Sentiment)
├── Complex explanations: "Why is this happening?" (use Language + Analytics)

Sentiment Analysis - Best for:
├── Team morale: "How is team morale this week?"
├── Communication health: "Are there any team tensions?"
├── Satisfaction trends: "Is job satisfaction improving?"
├── Early warning: "Are there signs of burnout?"

Risk Detection - Best for:
├── Project health: "What risks should I be worried about?"
├── Proactive planning: "What could go wrong with this release?"
├── Resource planning: "Are we at risk of overcommitment?"
├── Timeline assessment: "Will we hit our deadline?"

Context Classification - Best for:
├── Automatic routing: Happens behind the scenes
├── Intent understanding: Ensures you get the right type of response
├── Scope detection: Helps models understand what you're asking about
├── Personalization: Adapts responses to your role and context

Instead of vague questions:
❌ "How are things?"
✅ "How is the MOBILE project tracking against our sprint commitment?"

❌ "Any problems?"
✅ "What risks could impact our Q1 delivery for the PLATFORM project?"

❌ "Team status?"
✅ "Which team members on the Frontend team might be approaching burnout?"

❌ "What should I do?"
✅ "Given that we're 2 weeks behind on the API integration, what are our options to still hit the March 15 deadline?"

Be Specific About:
├── Project/Team: "Frontend team on MOBILE project" vs "the team"
├── Time Frame: "this week" vs "last 2 sprints" vs "Q1"
├── Scope: "API integration" vs "backend work" vs "the project"
├── Context: "for the executive update" vs "for sprint planning"
├── Audience: "explain to a junior developer" vs "executive summary"
├── Urgency: "urgent decision needed" vs "planning for next quarter"
├── Constraints: "within current budget" vs "if we had unlimited resources"
└── Success Criteria: "to hit our deadline" vs "to improve quality"

Provide Context:
├── Current Situation: What's happening now?
├── Background: What led to this situation?
├── Constraints: What limitations do you have?
├── Goals: What are you trying to achieve?
├── Stakeholders: Who is involved or affected?
├── Timeline: When do you need this resolved?
├── Resources: What resources are available?
└── Success Metrics: How will you measure success?

Break Complex Questions:
❌ "Analyze our team performance, identify risks, and create an action plan"
✅ First: "How is our team performing compared to last quarter?"
✅ Then: "Based on current performance, what risks should we be concerned about?"
✅ Finally: "What specific actions should we take to address these risks?"

Sequential Questioning:
├── Start broad: "How is the project going overall?"
├── Drill down: "What's causing the velocity decrease?"
├── Get specific: "How can we address the testing bottleneck?"
├── Plan action: "What's the timeline for implementing these changes?"

Build Context Gradually:
├── Session 1: Establish project context and current status
├── Session 2: Dive deeper into specific areas of concern
├── Session 3: Develop action plans and next steps
├── Session 4: Review progress and adjust plans

Reference Previous Conversations:
├── "Following up on yesterday's discussion about the API delays..."
├── "Regarding the risk we identified last week..."
├── "Building on the action plan we created..."
├── "Given the new information since our last conversation..."

Before Asking Questions:
├── ✅ Recent sync: Ensure integrations have synced recently (within 4 hours)
├── ✅ Complete data: Verify all relevant projects and teams are connected
├── ✅ Accurate assignments: Check that team members are assigned correctly
├── ✅ Updated status: Ensure ticket statuses are current
├── ✅ Proper labeling: Use consistent labels and components
├── ✅ Clear descriptions: Write clear, descriptive ticket summaries
├── ✅ Realistic estimates: Provide realistic story point estimates
└── ✅ Regular grooming: Keep backlog groomed and prioritized

Optimize Your Setup:
├── Connect all relevant tools: Jira, Slack, GitHub, etc.
├── Map custom fields: Ensure important custom fields are synced
├── Set up webhooks: Enable real-time updates where possible
├── Configure permissions: Ensure Impulsum can access all relevant data
├── Regular maintenance: Clean up old/irrelevant data periodically
├── Team participation: Encourage team to use integrated tools consistently
├── Documentation: Keep project documentation up to date
└── Feedback loops: Regularly provide feedback to improve model accuracy

Provide Rich Context:
├── Project Phase: "We're in the final sprint before release"
├── Team Composition: "5-person cross-functional team with 2 new members"
├── Methodology: "Using 2-week Scrum sprints with daily standups"
├── Constraints: "Fixed deadline due to conference demo"
├── Recent Changes: "Just added 3 new features to scope last week"
├── External Factors: "Waiting on API from vendor team"
├── Success Criteria: "Must be demo-ready with <5 critical bugs"
└── Stakeholder Context: "CEO will be presenting this at the conference"

Historical Context:
├── Previous Performance: "Last quarter we averaged 32 story points per sprint"
├── Lessons Learned: "We learned from the Q3 project that..."
├── Pattern Recognition: "This is similar to the PLATFORM project where..."
├── Success Stories: "The approach that worked for MOBILE was..."
├── Failure Analysis: "We want to avoid what happened with PROJECT-X..."
├── Seasonal Patterns: "Q4 is typically slower due to holidays"
├── Team Evolution: "The team has been together for 6 months now"
└── Organizational Changes: "Since the reorganization last month..."

Consider External Factors:
├── Organizational: Restructuring, new leadership, policy changes
├── Market: Competitive pressure, customer demands, industry trends
├── Technical: Technology changes, platform updates, security requirements
├── Resource: Budget constraints, hiring freezes, skill shortages
├── Regulatory: Compliance requirements, audit schedules, legal changes
├── Seasonal: Holidays, vacation periods, conference seasons
├── Cultural: Company culture, team dynamics, communication styles
└── Strategic: Company priorities, strategic initiatives, goal changes

Communicate Constraints:
├── Budget: "We have a fixed budget of $X for this project"
├── Timeline: "Hard deadline of March 15 for conference demo"
├── Resources: "Can't add more people due to hiring freeze"
├── Technical: "Must use existing technology stack"
├── Quality: "Zero tolerance for security vulnerabilities"
├── Scope: "Core features are non-negotiable"
├── Process: "Must follow SOX compliance procedures"
└── Stakeholder: "CEO has final approval on all major decisions"

Language Model Responses:
├── Conversational: Natural, engaging communication style
├── Structured: Organized with clear sections and bullet points
├── Actionable: Includes specific recommendations and next steps
├── Contextual: References your specific situation and constraints
├── Balanced: Presents multiple perspectives and considerations
├── Educational: Explains reasoning and provides learning opportunities
├── Personalized: Adapted to your role, experience, and preferences
└── Forward-looking: Includes implications and future considerations

Analytics Model Outputs:
├── Quantitative: Specific numbers, percentages, and metrics
├── Comparative: Comparisons to baselines, targets, and benchmarks
├── Trending: Trend analysis and directional indicators
├── Predictive: Forecasts and probability estimates
├── Confidence: Confidence intervals and uncertainty quantification
├── Visual: Charts, graphs, and data visualizations (described)
├── Segmented: Broken down by team, project, time period, etc.
└── Actionable: Insights that lead to specific actions

Specialized Model Insights:
├── Sentiment: Emotional tone and team morale indicators
├── Risk: Risk probability, impact, and mitigation recommendations
├── Context: Intent understanding and appropriate response routing
├── Patterns: Pattern recognition and anomaly detection
├── Predictions: Future state predictions with confidence levels
├── Recommendations: Specific, actionable recommendations
├── Alerts: Early warning signals and threshold breaches
└── Explanations: Clear explanations of findings and reasoning

Confidence Indicators:
├── High Confidence (>90%): "I'm confident that..." / "The data clearly shows..."
├── Medium Confidence (70-90%): "Based on current trends..." / "It appears that..."
├── Low Confidence (<70%): "There are indications that..." / "It's possible that..."
├── Uncertain: "The data is mixed..." / "More information needed..."
├── Speculative: "If current trends continue..." / "One possibility is..."
├── Conditional: "Assuming X remains constant..." / "If Y happens, then..."
├── Caveated: "With the caveat that..." / "Keep in mind that..."
└── Qualified: "In most cases..." / "Generally speaking..."

When to Seek Clarification:
├── Ambiguous responses: Ask for more specific information
├── Conflicting information: Ask for reconciliation of differences
├── Low confidence: Ask what additional data would help
├── Unexpected results: Ask for explanation of surprising findings
├── Missing context: Provide additional context and ask again
├── Unclear recommendations: Ask for more specific action steps
├── Technical complexity: Ask for simpler explanation if needed
└── Stakeholder communication: Ask for audience-appropriate version

From Insights to Action:
├── Immediate Actions: What can be done today/this week?
├── Short-term Plans: What should be planned for next 2-4 weeks?
├── Long-term Strategy: What needs to be considered for next quarter?
├── Resource Requirements: What resources are needed for implementation?
├── Success Metrics: How will you measure success of actions taken?
├── Risk Mitigation: What could go wrong and how to prevent it?
├── Stakeholder Communication: Who needs to be informed of actions?
└── Follow-up Plans: When and how to reassess progress?

Validation Steps:
├── Cross-check: Verify insights with your own observations
├── Team Input: Get team perspective on recommendations
├── Stakeholder Buy-in: Ensure stakeholder support for proposed actions
├── Feasibility Check: Confirm actions are realistic given constraints
├── Impact Assessment: Evaluate potential impact of proposed changes
├── Risk Assessment: Consider risks of both action and inaction
├── Timeline Validation: Ensure proposed timeline is achievable
└── Resource Confirmation: Confirm availability of required resources

Session 1: Discovery & Assessment
├── "Give me a comprehensive health check of all my projects"
├── "What are the top 3 risks across my portfolio?"
├── "Which teams are performing above/below expectations?"
├── "What patterns do you see in our delivery performance?"
└── Goal: Establish baseline understanding and identify focus areas

Session 2: Deep Dive Analysis
├── "Let's analyze the MOBILE project delays in detail"
├── "What's causing the Frontend team's velocity decrease?"
├── "Analyze the correlation between our quality metrics and delivery speed"
├── "What external factors are impacting our performance?"
└── Goal: Understand root causes and contributing factors

Session 3: Solution Development
├── "What are 5 different approaches to solve the API integration delays?"
├── "If we had to cut scope, what would you recommend and why?"
├── "How can we optimize our testing process to reduce cycle time?"
├── "What would a realistic recovery plan look like?"
└── Goal: Generate and evaluate potential solutions

Session 4: Implementation Planning
├── "Create a detailed action plan for the next 4 weeks"
├── "What resources do we need to implement these changes?"
├── "How should we communicate these changes to stakeholders?"
├── "What metrics should we track to measure success?"
└── Goal: Create concrete, actionable implementation plan

Building Context Across Conversations:
├── Reference Previous Discussions: "Following up on yesterday's risk analysis..."
├── Connect Related Topics: "This relates to the velocity issue we discussed..."
├── Build on Previous Insights: "Given what we learned about the testing bottleneck..."
├── Track Progress: "Since we implemented the changes we discussed..."
├── Evolve Understanding: "My thinking has evolved since our last conversation..."
├── Maintain Continuity: "To continue our discussion about resource allocation..."
├── Bridge Time Gaps: "It's been a week since we talked about this..."
└── Synthesize Learning: "Pulling together everything we've discussed..."

Advanced Context Management:
├── Explicit Context Setting: "Let me give you context on what's changed..."
├── Context Switching: "Let's shift focus from MOBILE to PLATFORM project..."
├── Context Layering: "In addition to the technical issues, we also have..."
├── Context Validation: "Do you have the right context about our team structure?"
├── Context Updates: "Here's what's changed since our last conversation..."
├── Context Prioritization: "The most important context for this discussion is..."
├── Context Boundaries: "For this conversation, let's focus only on..."
└── Context Integration: "How does this new information change our analysis?"

The Diagnostic Pattern:
├── Symptom Identification: "What symptoms are we seeing?"
├── Root Cause Analysis: "What's causing these symptoms?"
├── Impact Assessment: "What's the impact if we don't address this?"
├── Solution Options: "What are our options for addressing this?"
├── Recommendation: "What would you recommend and why?"
├── Implementation: "How should we implement this solution?"
├── Success Metrics: "How will we know if it's working?"
└── Follow-up: "When should we reassess progress?"

The Strategic Pattern:
├── Current State: "Where are we now?"
├── Desired State: "Where do we want to be?"
├── Gap Analysis: "What's the gap between current and desired state?"
├── Options Analysis: "What are our strategic options?"
├── Trade-off Analysis: "What are the trade-offs of each option?"
├── Recommendation: "What's the best path forward?"
├── Resource Requirements: "What resources do we need?"
└── Success Planning: "How will we measure and ensure success?"

The Innovation Pattern:
├── Challenge Definition: "What challenge are we trying to solve?"
├── Constraint Identification: "What constraints do we have to work within?"
├── Creative Exploration: "What creative approaches could we try?"
├── Feasibility Assessment: "Which approaches are most feasible?"
├── Risk Assessment: "What are the risks of each approach?"
├── Pilot Planning: "How could we test this approach?"
├── Scale Planning: "If successful, how would we scale this?"
└── Learning Integration: "How will we learn and iterate?"

Jira Optimization:
├── Consistent Naming: Use consistent project and component naming
├── Descriptive Summaries: Write clear, descriptive issue summaries
├── Proper Categorization: Use labels, components, and issue types consistently
├── Accurate Estimates: Provide realistic story point estimates
├── Status Updates: Keep issue statuses current and accurate
├── Clear Descriptions: Write detailed descriptions with acceptance criteria
├── Link Dependencies: Properly link related issues and dependencies
└── Regular Grooming: Regularly groom and update the backlog

Slack/Teams Optimization:
├── Channel Organization: Use channels consistently for different purposes
├── Thread Usage: Use threads for detailed discussions
├── Clear Communication: Write clear, professional messages
├── Context Sharing: Share relevant context in team communications
├── Decision Documentation: Document decisions in searchable channels
├── Status Updates: Provide regular status updates in team channels
├── Issue Escalation: Use appropriate channels for different types of issues
└── Knowledge Sharing: Share learnings and insights with the team

Webhook Configuration:
├── Real-time Updates: Enable webhooks for real-time data synchronization
├── Event Selection: Configure webhooks for relevant events only
├── Error Handling: Set up proper error handling and retry logic
├── Security: Use secure webhook endpoints with proper authentication
├── Monitoring: Monitor webhook delivery and performance
├── Documentation: Document webhook configuration and troubleshooting
├── Testing: Regularly test webhook functionality
└── Maintenance: Regularly review and update webhook configurations

Custom Field Mapping:
├── Important Fields: Map all business-critical custom fields
├── Consistent Mapping: Use consistent field mapping across projects
├── Data Types: Ensure proper data type mapping
├── Validation: Validate field mapping and data quality
├── Documentation: Document field mapping decisions and rationale
├── Regular Review: Regularly review and update field mappings
├── Team Training: Train team on proper custom field usage
└── Quality Monitoring: Monitor custom field data quality

Daily Maintenance:
├── Sync Status Check: Verify all integrations are syncing properly
├── Data Quality Spot Check: Quick check of recent data quality
├── Error Monitoring: Check for any sync errors or issues
├── Performance Monitoring: Monitor system performance and response times
└── User Feedback: Check for any user-reported data issues

Weekly Maintenance:
├── Comprehensive Sync Review: Review all integration sync status
├── Data Quality Analysis: Analyze data quality trends and issues
├── Performance Analysis: Analyze system performance and optimizestion opportunities
├── User Feedback Review: Review and address user feedback and issues
├── Configuration Review: Review and update configuration as needed
└── Team Communication: Communicate any issues or updates to the team

Monthly Maintenance:
├── Data Cleanup: Clean up old, irrelevant, or duplicate data
├── Configuration Optimization: Optimize configuration based on usage patterns
├── Performance Optimization: Optimize system performance and resource usage
├── User Training: Provide training on data quality best practices
├── Process Review: Review and improve data maintenance processes
└── Strategic Planning: Plan for future data and integration needs

Tool Integration Strategy:
├── Primary Tools: Jira (issues), Slack (communication), GitHub (code)
├── Secondary Tools: Confluence (docs), Figma (design), Notion (planning)
├── Monitoring Tools: DataDog (performance), Sentry (errors)
├── Business Tools: Salesforce (customers), HubSpot (marketing)
├── Analytics Tools: Mixpanel (product), Google Analytics (web)
├── Communication Tools: Zoom (meetings), Loom (async video)
├── Project Tools: Monday.com (planning), Asana (tasks)
└── Custom Tools: Internal APIs, databases, custom dashboards

Cross-Tool Correlation:
├── Issue-Code Correlation: Link Jira issues to GitHub commits/PRs
├── Communication-Work Correlation: Connect Slack discussions to work items
├── Performance-Work Correlation: Link performance metrics to development work
├── Customer-Development Correlation: Connect customer feedback to development priorities
├── Business-Technical Correlation: Link business metrics to technical work
├── Planning-Execution Correlation: Connect planning tools to execution tracking
├── Design-Development Correlation: Link design work to development implementation
└── Testing-Quality Correlation: Connect testing activities to quality metrics

Intelligent Webhook Management:
├── Event Filtering: Filter webhooks to only relevant events
├── Batch Processing: Batch similar webhook events for efficiency
├── Priority Queuing: Prioritize critical webhook events
├── Error Recovery: Implement robust error recovery and retry logic
├── Rate Limiting: Implement rate limiting to prevent overload
├── Security: Implement proper authentication and validation
├── Monitoring: Monitor webhook performance and reliability
└── Optimization: Continuously optimize webhook performance

Custom Webhook Development:
├── Business Logic: Implement custom business logic in webhooks
├── Data Transformation: Transform data as needed for Impulsum
├── Validation: Validate incoming webhook data
├── Enrichment: Enrich webhook data with additional context
├── Routing: Route webhooks to appropriate processing systems
├── Logging: Log webhook activity for debugging and analysis
├── Testing: Implement comprehensive webhook testing
└── Documentation: Document custom webhook implementations

API Usage Optimization:
├── Efficient Queries: Use efficient API queries to minimize data transfer
├── Caching: Implement intelligent caching to reduce API calls
├── Batch Operations: Use batch operations where possible
├── Rate Limit Management: Manage API rate limits effectively
├── Error Handling: Implement robust error handling and retry logic
├── Authentication: Use secure and efficient authentication methods
├── Monitoring: Monitor API usage and performance
└── Optimization: Continuously optimize API usage patterns

Custom API Development:
├── Business Requirements: Develop APIs that meet specific business needs
├── Data Models: Design appropriate data models for API responses
├── Security: Implement proper security and authentication
├── Performance: Optimize API performance and response times
├── Documentation: Provide comprehensive API documentation
├── Testing: Implement thorough API testing
├── Versioning: Implement proper API versioning strategies
└── Monitoring: Monitor custom API performance and usage

Explicit Feedback Methods:
├── Rating System: Rate AI responses for accuracy and helpfulness
├── Correction Tracking: Correct inaccurate information when you see it
├── Preference Feedback: Indicate preferences for response style and format
├── Feature Requests: Request new features or capabilities
├── Bug Reports: Report issues or problems with AI responses
├── Success Stories: Share examples of particularly helpful responses
├── Use Case Documentation: Document successful use cases and patterns
└── Improvement Suggestions: Suggest specific improvements to AI behavior

Implicit Feedback Patterns:
├── Follow-up Questions: Ask follow-up questions to clarify or expand
├── Action Taking: Take actions based on AI recommendations
├── Conversation Patterns: Develop consistent conversation patterns
├── Usage Frequency: Use certain features or models more frequently
├── Session Length: Spend more time on certain types of conversations
├── Query Refinement: Refine queries based on initial responses
├── Context Building: Build context over multiple conversations
└── Outcome Tracking: Track outcomes of AI-recommended actions

Personal Learning Strategies:
├── Experiment with Different Approaches: Try different questioning styles
├── Document Successful Patterns: Keep notes on what works well
├── Share Best Practices: Share successful approaches with team
├── Learn from Others: Learn from how others use Impulsum effectively
├── Regular Review: Regularly review and improve your usage patterns
├── Training Participation: Participate in training and learning opportunities
├── Community Engagement: Engage with user community for tips and tricks
└── Continuous Experimentation: Continuously try new approaches and techniques

Team Learning Strategies:
├── Best Practice Sharing: Share successful usage patterns across team
├── Collaborative Learning: Learn together as a team
├── Peer Training: Train team members on effective usage
├── Success Story Documentation: Document and share team success stories
├── Regular Reviews: Regularly review team usage and effectiveness
├── Process Integration: Integrate AI usage into team processes
├── Culture Development: Develop a culture of AI-enhanced work
└── Continuous Improvement: Continuously improve team AI usage

Success Metrics:
├── Time Savings: Measure time saved through AI assistance
├── Decision Quality: Assess improvement in decision-making quality
├── Problem Resolution: Track faster problem identification and resolution
├── Insight Generation: Measure increase in actionable insights
├── Productivity Gains: Track overall productivity improvements
├── Quality Improvements: Measure improvements in work quality
├── Satisfaction Increases: Track increases in job satisfaction
└── Business Impact: Measure business impact of AI-enhanced work

Measurement Strategies:
├── Before/After Comparison: Compare performance before and after AI adoption
├── Control Group Comparison: Compare AI users with non-users
├── Longitudinal Tracking: Track improvements over time
├── Qualitative Assessment: Gather qualitative feedback on impact
├── Business Metrics: Track business metrics affected by AI usage
├── User Surveys: Regular surveys on AI effectiveness and satisfaction
├── Case Studies: Develop detailed case studies of successful usage
└── ROI Calculation: Calculate return on investment of AI usage

Continuous Optimization:
├── Regular Assessment: Regularly assess AI usage effectiveness
├── Pattern Analysis: Analyze usage patterns for optimizestion opportunities
├── Feedback Integration: Integrate feedback into usage optimizestion
├── Process Refinement: Refine processes based on learning and feedback
├── Training Updates: Update training based on new learnings
├── Tool Optimization: Optimize tool configuration based on usage patterns
├── Team Coaching: Provide ongoing coaching for improved usage
└── Strategic Planning: Plan strategic improvements to AI usage

Vague Questions:
❌ "How are things?"
❌ "Any problems?"
❌ "What should I do?"
❌ "How's the team?"
❌ "Status update?"

Why These Don't Work:
├── Too broad: AI doesn't know what specific aspect you're interested in
├── No context: AI doesn't know which project, team, or timeframe
├── No scope: AI doesn't know the level of detail you need
├── No purpose: AI doesn't know why you're asking or how you'll use the info
└── No constraints: AI doesn't know your limitations or requirements

Better Alternatives:
✅ "How is the MOBILE project tracking against our sprint 23 commitment?"
✅ "What risks could impact our March 15 deadline for the API integration?"
✅ "Given our current velocity of 28 points/sprint, what's the realistic completion date for the remaining 84 points in the PLATFORM epic?"
✅ "Which Frontend team members show signs of burnout based on recent communication patterns?"
✅ "What's the status of the critical path items for our Q1 release?"

The 5W+H Framework:
├── Who: Which team, person, or stakeholder?
├── What: Which project, feature, or issue?
├── When: Which timeframe or deadline?
├── Where: Which environment, system, or location?
├── Why: What's the purpose or goal?
└── How: What level of detail or approach?

Specificity Checklist:
├── ✅ Project/Epic/Feature name mentioned
├── ✅ Team or individual specified
├── ✅ Timeframe clearly defined
├── ✅ Context or background provided
├── ✅ Purpose or goal stated
├── ✅ Constraints or limitations mentioned
├── ✅ Desired outcome or action specified
└── ✅ Audience or stakeholder identified

Start Broad, Get Specific:
├── Level 1: "How is the MOBILE project going overall?"
├── Level 2: "What's causing the velocity decrease in the last 2 sprints?"
├── Level 3: "How can we address the testing bottleneck that's slowing us down?"
├── Level 4: "What's the timeline and resource requirement for implementing automated testing?"

Build Context Over Time:
├── Session 1: Establish baseline understanding
├── Session 2: Identify specific issues or opportunities
├── Session 3: Develop solutions and action plans
├── Session 4: Plan implementation and track progress

Missing Context:
❌ Asking about "the project" when you manage multiple projects
❌ Referring to "last week" without specifying which week
❌ Mentioning "the team" when you work with multiple teams
❌ Using internal acronyms without explanation
❌ Assuming AI knows about recent changes or decisions

Stale Context:
❌ Continuing conversations after major changes without updating context
❌ Referencing old project structures or team compositions
❌ Using outdated timelines or deadlines
❌ Assuming AI knows about recent organizational changes
❌ Not updating context when switching between different topics

Conflicting Context:
❌ Providing contradictory information in the same conversation
❌ Switching contexts without clear transitions
❌ Mixing different projects or teams in the same discussion
❌ Using inconsistent terminology or naming
❌ Providing context that conflicts with integrated data

Context Setting:
✅ "Let me give you context on the MOBILE project: we're a 5-person cross-functional team..."
✅ "For background, we implemented a new testing process 2 weeks ago..."
✅ "Just to clarify, when I say 'the API team' I mean the Backend Infrastructure team..."
✅ "Since our last conversation, we've had a major scope change..."
✅ "This question is specifically about the Q1 release, not the overall project..."

Context Updates:
✅ "Before we continue, let me update you on what's changed..."
✅ "The situation has evolved since yesterday..."
✅ "We've made some decisions that change the context..."
✅ "There's new information that affects our previous discussion..."
✅ "The priorities have shifted, so let me give you the new context..."

Context Validation:
✅ "Do you have the right context about our team structure?"
✅ "Are you clear on the timeline and constraints we're working with?"
✅ "Should I clarify anything about the project background?"
✅ "Is there any context that would help you give a better answer?"
✅ "Let me know if you need more background on any aspect..."

Context Documentation:
├── Keep notes on key context provided to AI
├── Document major changes that affect context
├── Track different contexts for different projects/teams
├── Note successful context patterns for reuse
└── Share context best practices with team

Context Consistency:
├── Use consistent terminology across conversations
├── Maintain consistent project and team naming
├── Keep timelines and deadlines consistent
├── Use consistent role and responsibility definitions
└── Maintain consistent success criteria and goals

Context Evolution:
├── Update context as situations change
├── Acknowledge when context has shifted
├── Provide transition statements when switching contexts
├── Validate context understanding regularly
└── Build context progressively over multiple conversations

Overestimating Capabilities:
❌ Expecting AI to know information not in integrated systems
❌ Assuming AI can predict unpredictable external events
❌ Expecting 100% accuracy in all predictions and recommendations
❌ Assuming AI can make decisions that require human judgment
❌ Expecting AI to understand unspoken assumptions or context

Underestimating Capabilities:
❌ Only asking simple, factual questions
❌ Not leveraging AI for strategic thinking and planning
❌ Avoiding complex or nuanced questions
❌ Not using AI for creative problem-solving
❌ Limiting AI to basic data retrieval tasks

Misunderstanding AI Responses:
❌ Taking probabilistic statements as certainties
❌ Ignoring confidence levels and uncertainty indicators
❌ Not understanding the reasoning behind recommendations
❌ Expecting AI to have perfect knowledge of your specific situation
❌ Not recognizing when AI is making assumptions

What AI Does Well:
✅ Pattern recognition in large datasets
✅ Synthesizing information from multiple sources
✅ Generating multiple solution options
✅ Providing structured analysis and frameworks
✅ Offering different perspectives on problems
✅ Learning from feedback and improving over time
✅ Adapting communication style to your preferences
✅ Processing and analyzing complex information quickly

What AI Struggles With:
⚠️ Predicting truly random or unprecedented events
⚠️ Understanding unspoken organizational politics
⚠️ Making decisions that require ethical judgment
⚠️ Knowing information not available in integrated systems
⚠️ Understanding highly context-specific cultural nuances
⚠️ Predicting individual human behavior with high accuracy
⚠️ Making recommendations without sufficient data
⚠️ Understanding the full complexity of human relationships

How to Work With AI Limitations:
✅ Provide rich context to compensate for knowledge gaps
✅ Validate AI recommendations with your own judgment
✅ Use AI as a thinking partner, not a decision maker
✅ Combine AI insights with human expertise and intuition
✅ Recognize when problems require human-only solutions
✅ Use AI to generate options, then apply human judgment to choose
✅ Provide feedback to help AI learn and improve
✅ Set appropriate confidence levels for different types of decisions

Confidence Level Understanding:
├── High Confidence (>90%): Strong data support, clear patterns
├── Medium Confidence (70-90%): Good data, some uncertainty
├── Low Confidence (<70%): Limited data, high uncertainty
├── Speculative: Educated guesses based on limited information
├── Conditional: Depends on assumptions or external factors
├── Uncertain: Insufficient data for reliable prediction
├── Unknown: Information not available in current data
└── Requires Human Judgment: Decision requires human values/ethics

Response Interpretation:
├── "I'm confident that..." = High confidence, strong data support
├── "Based on current trends..." = Medium confidence, trend-based
├── "It appears that..." = Medium confidence, pattern recognition
├── "There are indications..." = Low confidence, weak signals
├── "It's possible that..." = Speculative, limited data
├── "If current conditions continue..." = Conditional prediction
├── "More information needed..." = Insufficient data
└── "This requires human judgment..." = Beyond AI capabilities

Incomplete Data:
❌ Missing team member assignments
❌ Incomplete project information
❌ Missing integration connections
❌ Outdated team structures
❌ Incomplete custom field mapping

Inconsistent Data:
❌ Inconsistent naming conventions
❌ Different status definitions across projects
❌ Inconsistent story point scales
❌ Mixed methodologies without clear boundaries
❌ Inconsistent priority definitions

Stale Data:
❌ Old project information not updated
❌ Team members who have left still assigned
❌ Completed projects not properly closed
❌ Integration sync failures not noticed
❌ Outdated process documentation

Poor Data Quality:
❌ Vague or unclear ticket descriptions
❌ Unrealistic or missing estimates
❌ Improper use of labels and components
❌ Missing acceptance criteria
❌ Poor linking of related issues

Data Audit Checklist:
├── ✅ All active projects connected and syncing
├── ✅ Team members properly assigned to projects
├── ✅ Integration sync status green across all tools
├── ✅ Custom fields mapped and syncing correctly
├── ✅ Naming conventions consistent across projects
├── ✅ Status definitions clear and consistently used
├── ✅ Story point scales consistent across teams
├── ✅ Labels and components used consistently
├── ✅ Dependencies properly linked and tracked
└── ✅ Completed work properly closed and archived

Data Maintenance Routine:
├── Daily: Check integration sync status
├── Weekly: Review data quality metrics and issues
├── Monthly: Audit project and team assignments
├── Quarterly: Review and update naming conventions
├── As needed: Clean up completed or cancelled projects
├── Ongoing: Train team on data quality best practices
├── Regular: Validate custom field mapping and usage
└── Continuous: Monitor and address data quality issues

Team Training:
├── Ticket Writing: Train on writing clear, descriptive tickets
├── Estimation: Train on consistent estimation practices
├── Status Updates: Train on keeping status current
├── Linking: Train on properly linking related work
├── Labeling: Train on consistent use of labels and components
├── Documentation: Train on maintaining good documentation
├── Process: Train on following established processes
└── Quality: Train on the importance of data quality

Quality Metrics:
├── Sync Success Rate: Percentage of successful integration syncs
├── Data Completeness: Percentage of complete vs incomplete records
├── Data Freshness: Average age of last update across records
├── Consistency Score: Measure of naming and categorization consistency
├── Accuracy Rate: Percentage of accurate vs inaccurate data
├── Usage Rate: Percentage of fields and features being used
├── Error Rate: Frequency of data errors and issues
└── User Satisfaction: User satisfaction with data quality

Monitoring Tools:
├── Integration Health Dashboards: Monitor sync status and performance
├── Data Quality Reports: Regular reports on data quality metrics
├── Error Alerts: Automated alerts for data quality issues
├── Usage Analytics: Track usage patterns and identify issues
├── User Feedback: Collect feedback on data quality issues
├── Audit Trails: Track changes and identify quality problems
├── Performance Monitoring: Monitor impact of data quality on AI performance
└── Improvement Tracking: Track improvements in data quality over time