measuring ai-assisted velocity without lying to yourself
📰 Dev.to AI
Learn to accurately measure AI-assisted velocity in your engineering team to avoid misleading metrics
Action Steps
- Track PR count and velocity over time to identify trends
- Analyze the backlog of AI-generated features to assess quality and completion rates
- Monitor refactors and debugging efforts to account for potential AI-induced technical debt
- Implement a data-driven approach to measure AI-assisted velocity
- Compare metrics across different teams and time periods to identify areas for improvement
Who Needs to Know This
Engineering leaders and team members can benefit from this knowledge to make informed decisions about AI tool adoption and optimization
Key Insight
💡 Measuring AI-assisted velocity requires a nuanced approach that considers multiple metrics and potential biases
Share This
🚀 Stop lying to yourself about AI-assisted velocity! Learn to measure it accurately and make data-driven decisions 📊
DeepCamp AI