💭 "I wish I could click on this card and be like, 'Clean this up.'" When a customer pointed out a flat, unstructured branch in her AI-generated opportunity solution tree, it sparked a three-week journey to fix the problem at its source. Here's what you'll learn from this article: How one customer complaint led to building 4 new AI evaluation metrics and testing 16 different experiment variations to get to the root cause—and why a quick fix wasn't the answer. Key takeaways: 🔍 Building reliable AI products requires more than just prompt engineering—you need evals, guardrails, and orchestration working together 📊 Calibrating your AI judges against production data is harder than it sounds; your judges can fail in ways you don't expect ⚖️ Fixing upstream errors (like poorly framed parents) is essential, but it can accidentally make downstream errors worse 🔄 Sometimes the best solution isn't tweaking prompts endlessly—it's building an agent that audits its own work and fixes mistakes 🎯 Sweating the details matters because the real benefit of an opportunity solution tree is knowing exactly what to work on next Read the article: ❓ What's one quality issue you've noticed in AI outputs that seemed simple to fix but turned out to be more complicated than expected? Share your thoughts in the comments below.