Building AI-Powered Products: Lessons from the Trenches

After shipping multiple AI products, I've learned that the gap between a compelling demo and a product people actually use is enormous. Getting a large language model to produce impressive output in a controlled demo takes an afternoon. Getting it to produce *reliable*, *useful* output for thousands of different users across thousands of different inputs — that's the actual engineering challenge. Here's what I've learned building in this space.

Start with the failure cases, not the happy path. Every AI product demo I've ever seen shows the model performing perfectly. But your users will find every edge case you didn't anticipate. Before I write a single line of production code now, I spend time trying to break the system. What happens when the input is ambiguous? When it's adversarial? When the model confidently returns something wrong? The products that survive are the ones that handle degraded states gracefully — with fallbacks, confidence signals, and clear paths for users to correct mistakes.

Latency is a product decision, not just a technical one. When I was building Jello SEO, I had to decide between a slower, more accurate pipeline and a faster, slightly noisier one. The right answer depended entirely on where the tool sat in a user's workflow. For a background batch job, users don't mind waiting. For an interactive feature, anything over two seconds starts to feel broken. The mistake I see teams make is treating AI response time as a pure engineering constraint rather than a UX constraint. Map out where the AI sits in your user's day and design latency targets from there.

The best AI products make the AI invisible. The most successful AI integrations I've seen don't lead with "AI-powered" in their marketing — they lead with the outcome. Users don't care about transformer architectures; they care about getting their job done faster. When I'm building, I ask: what would this feature look like if a brilliant, tireless human assistant were doing it? Then I try to make the AI match that experience as closely as possible. The moment your users start thinking about the model instead of their work, you've lost the thread.

Eval infrastructure is table stakes. This one hurt to learn the hard way. Early on, I would ship a prompt change and just... hope it was better. That's not engineering, that's gambling. Now before any significant model or prompt change ships, I run it against a test set of real inputs where I know the expected outputs. It doesn't have to be fancy — a spreadsheet with 50 representative examples and a script to score them will catch most regressions. The teams that build fast in AI aren't the ones moving recklessly — they're the ones who've built enough infrastructure to move confidently.