Shipping AI Features Responsibly
AI can transform your product, or quietly erode user trust. Here's the pragmatic framework we use to ship AI features that hold up in production.
Adding an AI feature is easy. Adding one that stays useful, safe, and affordable in production is the hard part. Over dozens of projects, we've landed on a few principles that keep AI features grounded in reality.
Solve a real problem, not a demo
The most common failure mode is building AI because it's exciting, not because it helps. Before writing a line of code, we ask:
- What specific task does this save the user time on?
- What does a good answer look like, and how will we measure it?
- What happens when the model is wrong, and how bad is that?
If we can't answer these clearly, the feature isn't ready.
Design for the wrong answer
Language models are confident even when they're incorrect. Good AI UX assumes that and plans for it:
Show sources, make edits easy, and never let the model take an irreversible action without a human in the loop.
Keep humans in control
The strongest AI features augment people rather than replace them. Draft, don't decide. Suggest, don't send. This keeps trust intact and keeps you out of trouble when the model slips.
Watch cost and latency like a hawk
AI features have a running meter. A few habits keep them sustainable:
- Cache and reuse results where inputs repeat.
- Use smaller models for simpler steps.
- Stream responses so the experience feels fast even when it isn't instant.
Measure, then iterate
Ship behind a flag, measure real outcomes, and improve. Track acceptance rates, corrections, and satisfaction, not just raw model accuracy.
Done well, AI features feel less like magic tricks and more like a thoughtful colleague, helpful, honest about limits, and always leaving you in charge.
