"The only way to know if our AI products and workflows are any good is with evals." 💡 If you're using AI to write PRDs, analyze customer feedback, or build customer-facing AI products, you need to understand evals. This guide breaks down what evals actually are and why product teams should be building them. Here's what you'll learn: 🔍 What evals are and why they're different from traditional software testing
📊 How to do error analysis to identify which mistakes matter most
🛠️ Four types of evals: golden datasets, code assertions, LLM-as-a-Judge, and customer feedback
✅ How to choose the right eval for each type of error
🔄 How to run experiments and measure if your changes actually work The key insight: with LLMs, you can't just test once and expect the same result. You need to measure how often your AI gets it right, and that starts with defining what "right" actually looks like for your specific product. Check out the article: ❓ What's one error you've noticed in an AI tool you use regularly? Share your thoughts in the comments below.