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How to Measure AI Coding Assistant Productivity: A Framework for Engineering Teams

· 11 min read
Scott Havird
Engineer

How to Measure AI Coding Assistant Productivity: A Framework for Engineering Teams

Here's a question I get asked constantly: "How do you know if AI coding tools are actually making your team more productive?"

It's a fair question. Engineering leaders are investing real budget in Claude Code, Cursor, and GitHub Copilot seats. Developers are restructuring their workflows around these tools. But when someone asks for data — actual numbers on impact — most teams have nothing to show.

I've been working on this problem for over a year, first as an engineering leader trying to justify AI tooling investments at Georgia-Pacific, and then by building PromptConduit to close the analytics gap. Here's the framework I've developed for measuring what actually matters.

PromptConduit: Building Analytics for AI Coding Assistants

· 6 min read
Scott Havird
Engineer

PromptConduit: Building Analytics for AI Coding Assistants

Every day, I spend hours having conversations with AI coding assistants. Claude Code helps me debug issues, Cursor generates components, and Gemini CLI answers quick questions. But here's the thing: I had no idea what I was actually asking them. What patterns emerged from my prompts? Which tools got invoked most frequently? Was I getting better at prompting over time?

These questions led me to build PromptConduit.