HOW TO BUILD AI PRODUCTS THAT ARE ACTUALLY GOOD

Summary from the TLDR Founders newsletter

Making AI work in production takes more than clever prompts. When thousands of users interact with a model, every mistake – wrong tone, bad data, off response – multiplies fast. That's why companies now rely on “evals,” systems that test and score models the same way engineers test code. Tools like Braintrust let teams log real user inputs, compare outputs against expected answers, and continuously tune models based on results. It's how Notion, Ramp, and Stripe keep AI features reliable as they scale.