Been building AI features for a while now. Here are the 3 biggest mistakes I see founders make:

1. Starting with the model instead of the problem.

You don't need GPT-4 for everything. Most AI features work fine with smaller, faster models. Define the user problem first, then pick the cheapest model that solves it. You can always upgrade later.

2. Skipping the eval pipeline.

If you can't measure whether your AI feature is actually working, you're flying blind. Before you ship anything, set up automated evals. Even simple ones (accuracy on 50 test cases) save you from shipping garbage.

3. Building the whole thing before testing with users.

Ship a prototype with a hardcoded prompt and a single API call. Get it in front of 5 users. The feedback will completely change your architecture — guaranteed.

I'm building a community for founders working through exactly these problems. Come join DeepBuild if you want to build AI products alongside people who actually ship.