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Why Your AI Coding Assistant Might Be Lying to You (Precision & Recall Explained) Analytics Table

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Why Your AI Coding Assistant Might Be Lying to You (Precision & Recall Explained) Most AI coding assistants don't measure whether the context they're given is actually useful — they just stuff files into a prompt and hope for the best. In this video, we break down precision and recall, two simple but powerful metrics that turn "does this feel like it's working?" into a hard, measurable engineering signal. You'll learn: - Why busy-looking AI activity (opening files, scanning repos) doesn't mean real progress - What "recall" measures — did the AI get all the files it actually needed? - What "precision" measures — did the AI avoid irrelevant noise and stay focused? - Why engineering leaders need objective metrics instead of subjective "it feels right" judgments - How to move from a hope-driven approach to a measurable, repeatable context evaluation process If you're an engineering manager, CTO, or VP of Engineering rolling out AI coding tools across your team, this is the framework you need to evaluate whether your AI is solving the right problem — or just spinning its wheels. You can't improve what you don't measure. How are you measuring your AI's context quality? #AICoding #SoftwareEngineering #EngineeringLeadership

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