From the course: AI Foundations: Ideating and Prototyping
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Refining based on feedback
From the course: AI Foundations: Ideating and Prototyping
Refining based on feedback
- Once the pitch is delivered and the funding starts rolling in, the real work begins. Imagine standing in front of stakeholders thrilled about your AI product's potential only to hear post-launch, "This isn't quite what we needed." That's the risk of skipping one crucial step, refining your product based on feedback. Feedback is the lifeblood of innovation. It's how great products evolve from good ideas. Here's how to extract value from feedback and transform it into actionable improvements that ensure your AI product hits the mark. Understanding feedback isn't always straightforward. It often comes unpolished, vague, or emotionally charged. Think of feedback as raw data. Your job is to mine it, identify patterns, and prioritize key insights. If multiple users highlight that your AI tool struggles with edge cases, that's your cue to dig deeper. Ask probing questions to clarify vague comments. Look for common themes to uncover systematic issues. By actively listening, you'll turn…
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