Engineering Enablement by DX Running data-driven evaluations of AI engineering tools
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Dec 12, 2025 
Abi Noda, CEO of DX and a developer productivity expert, joins Laura Tacho to discuss the rapidly evolving landscape of AI engineering tools. They delve into the importance of data-driven evaluations, outlining practical methods for shortlisting tools and structuring trials that reflect real development workflows. Noda emphasizes the need for clear goals and representative cohorts to measure effectiveness. The conversation highlights essential frameworks, like the AI Measurement Framework, to ensure impactful tool adoption and avoid costly missteps.
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Begin With A Clear Research Question
- Start evaluations with a clear research question tied to a business outcome, not just curiosity about a new tool.
- Work backward from that goal to design metrics, cohorts, and trial scope for reliable results.
Keep Shortlists Small And Scalable
- Shortlist a small set of tools (commonly 2–3 plus your incumbent) to keep trials manageable.
- Scale the number of simultaneous tools to your developer population and experiment capacity.
Group Tools By Use Case And Mode
- Group tools by use case and interaction mode and run separate evaluations per category.
- Avoid comparing agentic IDEs directly against chat-only assistants because they serve different needs.