AI & Model Issues

Common Signs:
├── AI says project is "on track" but you know it's delayed
├── Team member assignments are wrong or outdated
├── Numbers don't match what you see in Jira/other tools
├── Historical data seems incorrect or incomplete
├── Recommendations don't make sense for your situation
├── AI references projects or people that don't exist
├── Dates and timelines are completely off
└── Quality metrics don't align with reality

Data Sync Issues:
Problem: AI using stale or incomplete data
├── Check: Settings > Integrations > Last Sync Time
├── Solution: Manual sync + wait 5 minutes, ask again
├── Prevention: Enable webhooks for real-time updates
└── Escalation: If sync fails repeatedly, contact support

Permission Problems:
Problem: AI can't see all data you can see
├── Check: Your Jira permissions vs Impulsum integration permissions
├── Solution: Reconnect integration with proper permissions
├── Prevention: Use admin account for integration setup
└── Escalation: Contact Jira admin to verify permissions

Custom Field Mapping:
Problem: Important fields not synced to Impulsum
├── Check: Settings > Integrations > Field Mapping
├── Solution: Add missing custom fields to sync configuration
├── Prevention: Map all business-critical fields during setup
└── Escalation: Contact support for complex field mapping

Data Processing Lag:
Problem: Recent changes not reflected in AI responses
├── Check: When was the last data update?
├── Solution: Wait 15 minutes for processing, or specify time context
├── Prevention: Ask "based on data from [specific time]"
└── Escalation: If lag exceeds 1 hour, check system status

Cross-Reference Verification:
├── Compare AI response with source system (Jira, etc.)
├── Check multiple data points for consistency
├── Verify with team members who have direct knowledge
├── Look at historical patterns to identify anomalies
├── Use different question phrasings to test consistency
├── Check confidence levels in AI responses
├── Validate against known facts and recent events
└── Test with simple, verifiable questions first

Data Quality Checks:
├── Verify integration sync status and timing
├── Check for missing or incomplete project data
├── Validate team member assignments and roles
├── Confirm project timelines and milestones
├── Check custom field mapping and values
├── Verify status definitions and workflows
├── Validate historical data completeness
└── Check for data corruption or inconsistencies

Context Problems:
├── AI answers about wrong project when you manage multiple
├── Includes team members no longer on project
├── References old sprints or archived data
├── Misunderstands time references ("last week", "recently")
├── Confuses different teams or departments
├── Uses wrong methodology assumptions (Scrum vs Kanban)
├── Misinterprets your role or authority level
└── Doesn't understand organizational structure

Be More Specific:
❌ Vague: "How's the team?"
✅ Specific: "How's the Frontend team on the MOBILE project this week?"

❌ Ambiguous: "What happened with the bugs?"
✅ Clear: "What bugs were created in the MOBILE project in the last 3 days?"

❌ Generic: "Any risks?"
✅ Targeted: "What risks could impact the March 15 deadline for the API integration in the PLATFORM project?"

Set Clear Boundaries:
├── Project scope: "For the MOBILE project specifically..."
├── Time boundaries: "Looking at the last 2 sprints..."
├── Team scope: "For the Frontend team only..."
├── Role context: "From a Project Manager perspective..."
├── Stakeholder context: "For the executive update..."
├── Methodology context: "Using our Scrum process..."
├── Priority context: "Focusing on critical path items..."
└── Constraint context: "Given our March deadline..."

Progressive Context Building:
├── Session 1: Establish project and team context
├── Session 2: Add timeline and constraint context
├── Session 3: Include stakeholder and priority context
├── Session 4: Refine based on feedback and results

Context Correction:
├── Immediate correction: "No, I meant the PLATFORM project, not MARKETING"
├── Context reset: "Let's talk about a different topic now: [new topic]"
├── Context update: "Since our last conversation, the situation has changed..."
├── Context validation: "Do you understand that we're using Kanban, not Scrum?"

Context Maintenance:
├── Regular updates: Update context when situations change
├── Consistency checks: Ensure context remains consistent across conversations
├── Context documentation: Keep notes on key context for complex projects
├── Team alignment: Ensure team uses consistent context and terminology

Multi-Dimensional Context:
├── Temporal: Current sprint, quarter, project phase
├── Organizational: Team structure, reporting relationships
├── Technical: Technology stack, architecture, constraints
├── Business: Goals, priorities, success criteria
├── Process: Methodology, ceremonies, workflows
├── Cultural: Communication style, decision-making approach
├── External: Market conditions, regulatory requirements
└── Historical: Past performance, lessons learned

Context Layering:
├── Base layer: Fundamental project and team information
├── Situational layer: Current circumstances and challenges
├── Constraint layer: Limitations and requirements
├── Goal layer: Objectives and success criteria
├── Stakeholder layer: Key people and their interests
├── Process layer: How work gets done
├── Cultural layer: How decisions are made
└── Strategic layer: Long-term vision and direction

Model Mismatch Signs:
├── Getting conversational responses when you need data
├── Getting data dumps when you need strategic advice
├── Overly technical responses for business questions
├── Overly simple responses for complex technical issues
├── Wrong level of detail for your role or audience
├── Inappropriate tone for the situation
├── Missing specialized analysis (sentiment, risk, etc.)
└── Generic responses that don't leverage your data

Question Framing for Better Routing:
├── For Analytics: "What does the data show about..."
├── For Strategy: "How should I approach..." or "What would you recommend..."
├── For Sentiment: "How is the team feeling about..." or "What's the team mood..."
├── For Risk: "What risks should I be concerned about..." or "What could go wrong..."
├── For Forecasting: "When will we..." or "What's the probability of..."
├── For Explanation: "Why is..." or "Explain the reasoning behind..."
├── For Planning: "How should we plan..." or "What's the best approach to..."
└── For Troubleshooting: "What's causing..." or "How do I fix..."

Explicit Model Requests:
├── "Give me the data analysis on..."
├── "I need strategic advice on..."
├── "Analyze the team sentiment around..."
├── "Assess the risks of..."
├── "Forecast the timeline for..."
├── "Explain in detail why..."
├── "Help me plan the approach to..."
└── "Troubleshoot the issue with..."

Response Refinement:
├── Ask for different perspective: "Give me a different angle on this"
├── Request specific analysis: "Focus on the technical aspects"
├── Change detail level: "Give me a high-level executive summary"
├── Adjust audience: "Explain this for a junior developer"
├── Request specific format: "Give me this as bullet points"
├── Ask for examples: "Provide specific examples"
├── Request action items: "What are the specific next steps?"
└── Seek alternatives: "What are other ways to approach this?"

Quality Validation:
├── Cross-check with multiple questions
├── Ask for reasoning and evidence
├── Request confidence levels
├── Validate against your knowledge
├── Test with simpler questions first
├── Compare responses over time
├── Get second opinions from team
└── Provide feedback on response quality

Performance Problems:
├── AI responses very slow (>30 seconds)
├── Timeouts or "processing" messages
├── Incomplete responses that cut off mid-sentence
├── Error messages about capacity or availability
├── Responses that seem rushed or low-quality
├── Inconsistent response times
├── System appears unresponsive
└── Frequent "try again later" messages

System Status Check:
├── Visit status.impulsum.com for current system status
├── Check for ongoing maintenance or incidents
├── Verify your internet connection stability
├── Test with simple questions first
├── Try different browsers or devices
├── Check if issue is widespread or specific to you
├── Review recent system announcements
└── Contact support if status shows all green but issues persist

Query Optimization:
❌ Complex: "Analyze all projects for the last 6 months and predict which ones will be delayed based on team capacity, technical debt, and stakeholder satisfaction while considering upcoming holidays and budget constraints"

✅ Simplified: "Which projects are most at risk of delays this quarter?"
→ Follow up: "What's causing the risk in the MOBILE project?"
→ Then: "How do upcoming holidays affect this timeline?"

Break Down Complex Requests:
├── Start with overview question
├── Follow up with specific details
├── Ask for one analysis at a time
├── Build complexity gradually
├── Allow processing time between requests
├── Save complex analysis for off-peak hours
├── Use multiple shorter conversations instead of one long one
└── Provide clear, specific context for each question

Usage Timing:
├── Peak hours (9am-5pm EST): Slower responses expected
├── Off-peak hours: Faster responses, better for complex analysis
├── Weekend usage: Generally faster response times
├── Holiday periods: May have reduced capacity
├── Maintenance windows: Check status page for scheduled maintenance
├── High-traffic periods: End of sprint, quarter, year
├── Optimal times: Early morning, late evening in your timezone
└── Emergency usage: Critical issues get priority processing

Plan Considerations:
├── Starter plan: Lower priority processing, longer queues
├── Pro plan: Standard priority processing
├── Teams plan: Higher priority processing
├── Enterprise plan: Highest priority, dedicated resources
├── Usage limits: Check current usage against plan limits
├── Overage handling: Understand what happens when limits exceeded
├── Upgrade options: Consider upgrading for better performance
└── Support priority: Higher plans get faster support response

Efficient Usage Patterns:
├── Batch related questions in single conversation
├── Use specific, focused questions
├── Provide context upfront to avoid back-and-forth
├── Use follow-up questions instead of repeating context
├── Save complex analysis for when you have time to wait
├── Use simple questions for quick status checks
├── Leverage conversation history instead of starting fresh
└── Provide feedback to help AI learn your preferences

Resource Management:
├── Monitor your usage against plan limits
├── Use analytics to understand usage patterns
├── Optimize question efficiency
├── Share insights with team to reduce duplicate queries
├── Use scheduled reports instead of repeated manual queries
├── Cache important insights for reuse
├── Document successful query patterns
└── Train team on efficient usage practices

Consistency Testing:
├── Ask the same question multiple times
├── Rephrase questions to test understanding
├── Test with different contexts
├── Compare responses across different sessions
├── Test with known facts vs unknown information
├── Validate responses against source data
├── Check for bias or systematic errors
└── Test edge cases and unusual scenarios

Response Quality Assessment:
├── Accuracy: How factually correct are the responses?
├── Relevance: How well do responses address your question?
├── Completeness: Are responses comprehensive enough?
├── Clarity: Are responses clear and understandable?
├── Actionability: Do responses include actionable insights?
├── Consistency: Are responses consistent across similar questions?
├── Confidence: Are confidence levels appropriate?
└── Usefulness: Do responses provide genuine value?

Capability Boundaries:
├── Test with simple factual questions
├── Progress to complex analytical questions
├── Test creative and strategic thinking
├── Evaluate prediction accuracy
├── Test with ambiguous or unclear questions
├── Evaluate handling of conflicting information
├── Test with incomplete or missing data
├── Assess performance with edge cases

Specialization Testing:
├── Test sentiment analysis with known team situations
├── Test risk detection with known project risks
├── Test forecasting with historical data
├── Test context classification with different question types
├── Evaluate language model performance across different topics
├── Test analytics model accuracy with verifiable data
├── Assess integration between different models
└── Evaluate overall system orchestration

Response Debugging:
├── Ask AI to explain its reasoning
├── Request confidence levels for responses
├── Ask for alternative perspectives
├── Request source information or data references
├── Ask for assumptions being made
├── Request step-by-step analysis
├── Ask for uncertainty quantification
└── Request validation of key facts

Model Selection Debugging:
├── Explicitly request specific model types
├── Test routing with different question formats
├── Analyze which models are being used for different queries
├── Test model switching within conversations
├── Evaluate appropriateness of model selection
├── Test override capabilities for model selection
├── Analyze model combination effectiveness
└── Evaluate orchestration decision-making

Integration Health Check:
├── Verify all integrations are connected and active
├── Check last sync times for all data sources
├── Validate webhook delivery and processing
├── Test manual sync functionality
├── Check for sync errors or failures
├── Validate data transformation and processing
├── Verify data storage and retrieval
└── Test end-to-end data flow

Data Quality Validation:
├── Compare Impulsum data with source systems
├── Check for missing or incomplete records
├── Validate data freshness and currency
├── Test data consistency across different views
├── Check for data corruption or errors
├── Validate custom field mapping and values
├── Test data filtering and access controls
└── Verify data retention and archival

Source System Verification:
├── Log into source systems (Jira, Slack, etc.)
├── Verify data exists and is accessible
├── Check permissions and access rights
├── Validate data format and structure
├── Test API connectivity and responses
├── Check for rate limiting or throttling
├── Verify webhook configuration and delivery
└── Test data export and import functionality

Data Transformation Testing:
├── Check data mapping and transformation rules
├── Validate field mapping and data types
├── Test data enrichment and augmentation
├── Check data normalization and standardization
├── Validate data aggregation and summarization
├── Test data filtering and selection
├── Check data validation and quality rules
└── Verify data privacy and security measures

Real-Time Monitoring:
├── Monitor integration sync status and performance
├── Track data processing times and throughput
├── Monitor error rates and failure patterns
├── Track data quality metrics and trends
├── Monitor system resource usage and performance
├── Track user access patterns and usage
├── Monitor API usage and rate limiting
└── Track data storage and retention

Diagnostic Tools:
├── Integration health dashboards
├── Data quality reports and metrics
├── Error logs and diagnostic information
├── Performance monitoring and profiling
├── User activity and usage analytics
├── System health and resource monitoring
├── API usage and performance tracking
└── Data lineage and flow visualization

Context Validation:
├── Ask AI to summarize its understanding of context
├── Test context persistence across conversation turns
├── Validate context switching and management
├── Check context inheritance and propagation
├── Test context conflict resolution
├── Validate context priority and weighting
├── Check context expiration and refresh
└── Test context sharing and isolation

Context Debugging Questions:
├── "What do you understand about my current project?"
├── "What context do you have about my team?"
├── "What assumptions are you making about my role?"
├── "What information do you have about our methodology?"
├── "What do you know about our current timeline?"
├── "What context do you have about our constraints?"
├── "What do you understand about our goals?"
└── "What information might be missing for better responses?"

Context Building Strategies:
├── Provide context incrementally across conversations
├── Use consistent terminology and naming
├── Establish clear boundaries and scope
├── Provide explicit context updates when situations change
├── Use context validation questions regularly
├── Document successful context patterns
├── Share context best practices with team
└── Maintain context consistency across team members

Context Management:
├── Regular context updates and maintenance
├── Context documentation and sharing
├── Context validation and verification
├── Context conflict identification and resolution
├── Context optimizestion and refinement
├── Context training and education
├── Context monitoring and analytics
└── Context feedback and improvement

Multi-Layered Context:
├── Immediate context: Current conversation and immediate needs
├── Session context: Current work session and related activities
├── Project context: Current project status and characteristics
├── Team context: Team composition, dynamics, and performance
├── Organizational context: Company structure, culture, and processes
├── Temporal context: Current time period, deadlines, and milestones
├── Strategic context: Long-term goals and strategic direction
└── Environmental context: External factors and constraints

Context Debugging Tools:
├── Context summary requests
├── Context validation questions
├── Context comparison across conversations
├── Context persistence testing
├── Context conflict identification
├── Context effectiveness measurement
├── Context optimizestion recommendations
└── Context training and improvement

Response Time Analysis:
├── Measure response times for different question types
├── Track response time trends over time
├── Compare performance across different models
├── Analyze performance by time of day and usage patterns
├── Measure performance impact of context complexity
├── Track performance across different user roles and plans
├── Analyze performance correlation with system load
└── Measure performance impact of data volume and complexity

Quality vs Speed Trade-offs:
├── Analyze accuracy vs response time relationships
├── Test performance impact of different quality settings
├── Measure trade-offs between depth and speed
├── Analyze performance impact of multiple model usage
├── Test performance optimizestion strategies
├── Measure user satisfaction with different performance levels
├── Analyze business impact of performance variations
└── Optimize performance for different use cases and priorities

Query Optimization:
├── Use specific, focused questions
├── Provide context upfront to reduce back-and-forth
├── Break complex questions into simpler parts
├── Use follow-up questions instead of repeating context
├── Batch related questions in single conversations
├── Use appropriate level of detail for your needs
├── Leverage conversation history effectively
└── Provide clear success criteria and constraints

Usage Optimization:
├── Time usage for optimal performance periods
├── Use appropriate plan level for your performance needs
├── Monitor usage against plan limits and quotas
├── Optimize team usage patterns and coordination
├── Use caching and reuse strategies effectively
├── Leverage scheduled reports and automation
├── Share insights and reduce duplicate queries
└── Train team on efficient usage practices

Performance Monitoring:
├── Track personal usage patterns and performance
├── Monitor team usage and performance trends
├── Analyze performance correlation with business outcomes
├── Track performance improvement over time
├── Monitor system performance and capacity
├── Analyze performance impact of different strategies
├── Track user satisfaction with performance
└── Measure ROI of performance optimizestion efforts

Performance Troubleshooting:
├── Identify performance bottlenecks and constraints
├── Analyze root causes of performance issues
├── Test performance optimizestion strategies
├── Measure impact of optimizestion efforts
├── Monitor performance regression and improvement
├── Coordinate with support for performance issues
├── Share performance best practices with team
└── Continuously improve performance optimizestion strategies