AI
Building AI features that actually ship
By Hannah Lee, Lead Data Scientist · Published on · 6 minute read
Most AI prototypes never reach production. These are the lessons we learned from the ones that did.
It has never been easier to build an impressive AI demo. It has also never been easier to spend six months on a prototype that nobody uses. Here is what separates the two.
Define “good enough” before you start
Before writing any code, agree with the business on a measurable target. “Classify 90% of support tickets correctly” is a goal. “Use AI to improve support” is a wish.
Build the evaluation first
We collect a few hundred real examples and label them by hand. That small dataset becomes the test every change must pass. Without it, you are just guessing.
Keep a human in the loop
The most successful AI features we have shipped don't replace people — they help people work faster. Suggestions, drafts and summaries are easier to trust than fully automatic decisions.
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