Want To Build Better AI Products, Faster? Build This First

Want better AI products? Well, you need to accelerate your experimentation cycle. How do you do that? Build an evaluation harness before you start testing your eval harness is really your lab bench, not a side project. It is the thing that accelerates your ability to conduct the scientific method on your AI product faster. When it's in place, you can test variations A through Z in hours, not weeks. And so you might ask how do you build one high level? Number one Create a golden data set of a hundred curated user questions and ideal responses Input output pairs for an AI health coach. For example, this might be I'm pre diabetic and just started keto. What should I eat after a workout? Number two Include past critical failures like when the bot gave unsafe health advice or didn't disclose a necessary disclaimer because before giving advice. Number three Test different logic paths Nutrition, mental health, chronic conditions. Number four Pin your LLM judge model version to keep evaluations consistent across updates. That's going to help a lot. Number five Use both code based checks like no restricted terms and LLM based scoring for nuance. Lastly, bonus tip. Split your data. So tune your responses on a validation data set, then verify generalization with a test set. That's how you improve fast without overfitting. The lesson here is build your bench before you start experimenting. If you invest in the infrastructure first rate and get more velocity later and that's how you ship safer, smarter and faster AI with a focus on evals. That's something simple that I learned from this AI evals course by Hamil and Shreya. One of the hottest courses on Maven right now. They brought in a guest speaker, a data scientist from Amazon, Eugene Yan highly recommend all of their work. Check them out.