What I Learned Shipping 3 AI Products This Month

Product 1: AI Writing Assistant

Lesson: Context is everything.

The model was good. The prompt was good. What killed the first version was that it had no context — it didn't know what the user had written before, what tone they preferred, or what they were trying to accomplish.

Adding a simple "recent history" context window improved output quality more than any prompt engineering.

Product 2: Smart Data Explorer

Lesson: Natural language is harder than it looks.

"Ask questions about your data in plain English" sounds great until you realize people ask questions in wildly different ways. The lesson: don't try to handle every possible phrasing. Instead, give people a starting point — suggested questions, examples, templates — and let them modify from there.

Product 3: Prompt Engineering Toolkit

Lesson: Open source builds trust.

This one I open-sourced from day one. The result? More feedback, more contributors, more credibility. For tools aimed at developers, open source isn't just a distribution strategy — it's a product feature.

The Big Takeaway

The model matters, the prompt matters, but the UX matters more. Every product that succeeded did so because it made the AI's capability accessible — not because it had the smartest model or the longest prompt.