First Questions

💬 Asking Effective Questions to the AI

🎯 Types of Powerful Questions

📊 Analysis Questions

What’s really happening?

🔮 Predictive Questions

What’s going to happen?

• “Are we going to complete the sprint on time?”
• “What projects are at risk of being delayed?”
• “What’s the optimal capacity for the next sprint?”
• “When do we estimate finishing project X?”

💡 Recommendation Questions

What should I do?

• “What tasks should I prioritize this week?”
• “How can I improve the team’s velocity?”
• “Which developer should I assign task Y to?”
• “What projects need more resources?”

🏆 Question Examples by Role

👔 Project Manager

📊 Executive Summary - Week of January 15

🟢 In good shape (3 projects):
• PLATFORM v2.0 - 78% completed, no risks
• Mobile App - 45% completed, 3 days ahead
• Analytics Dashboard - 90% completed, ready for release

🟡 Needs attention (2 projects):
• Marketing Website - 60% completed
  ⚠️ Risk: External dependency (designs)
  📋 Action: Review with Design Team on Wednesday

🔴 Critical (1 project):
• Customer Portal - 35% completed
  ⚠️ High Risk: 2 weeks delayed
  📋 Urgent actions:
    1. Reassign 1 developer from PLATFORM team
    2. Reduce MVP scope
    3. Emergency meeting tomorrow 9am

🏃 Scrum Master

📈 Recommendation for Sprint 25:

📊 Historical analysis (last 6 sprints):
• Average velocity: 42 story points
• Best sprint: 48 SP (Sprint 22)
• Worst sprint: 36 SP (Sprint 21 - had 2 holidays)

👥 Current context:
• Full team: 5 developers available
• No holidays or vacations planned
• 2 critical bugs pending (equivalent to -4 SP)

🎯 Recommendation: 38-40 story points
• Conservative but realistic
• Buffer for critical bugs
• 90% probability of success

Would you like me to analyze which specific tickets to include?

👨‍💼 Engineering Manager

👩‍💻 Workload Analysis - Ana García (Frontend)

📊 Comparison with the team:
• Ana: 28 assigned tickets (6 more than average)
• Team average: 22 tickets
• Luis: 25 tickets | María: 19 tickets | Carlos: 21 tickets

🎯 Task types - Ana vs. Team:
Ana: 40% critical bugs, 35% features, 25% tech debt
Team: 25% bugs, 50% features, 25% tech debt

💡 Insights:
• Ana receives more critical bugs (possible expertise tax)
• Her tickets are 15% more complex (avg 5.2 SP vs 4.5 SP)
• Average resolution time: same as team

🎯 Recommendations:
1. Redistribute 2-3 critical bugs to Luis and Carlos
2. Balance with more features (Ana prefers them)
3. Consider mentorship role (seems to be go-to person)

🧠 Advanced Techniques

🔍 Follow-up Questions

Don’t settle for the first answer. Ask follow-up questions:

1. Initial question: “What projects are delayed?”
2. Follow-up: “Why specifically was project X delayed?”
3. Action: “What can I do to recover the lost time?”
4. Implementation: “Who should I reassign to speed this up?”

📈 Combine Multiple Dimensions

Instead of asking only about time, combine metrics:

• “What projects are delayed AND have high business priority?”
• “Which developers are available AND have React experience?”
• “What bugs are critical AND easy to resolve quickly?”

🎯 Contextual Questions

The AI understands implicit context. Leverage it:

• “The CEO asked about project X. What should I tell them?”
• “We have a demo tomorrow. What features are ready to show?”
• “It’s Friday and there are 3 critical bugs. Which one should we solve first?”

❌ Questions That Don’t Work Well

🚫 Too Vague

❌ Bad: “How are things going?” ✅ Better: “How is the current sprint compared to what was planned?”

❌ Bad: “Are there any problems?” ✅ Better: “What tickets are blocked and need escalation?”

🚫 Without Temporal Context

❌ Bad: “What’s the team’s velocity?” ✅ Better: “What was the team’s velocity for the last 3 sprints?”

🚫 Questions About Information We Don’t Have

❌ Bad: “How much does developer Juan cost?” (we don’t have salary data) ✅ Better: “What’s Juan’s productivity vs. the rest of the team?”

🧪 Questions Playground

Templates by Situation

🎯 Practical Exercise

🚀 Next Step