Don’t Over-AI It: A Builder’s Guide
Introduction
AI is HOT: It's on every slide, in every pitch, and across every roadmap. While the hype is real, most AI features don’t work the way people think they would. The problem isn’t the technology; it’s the lazy thinking behind it. Here’s how to cut through the noise and build something that actually works.
Start with the Job, Not the Model
The question to ask isn’t, “Where can we add AI?” Instead, it’s “What job are we helping the user get done?” If you’re solving a real pain, the model doesn’t matter. AI won’t fix poor UX; it just adds cost and complexity to it.
Where Trust Quietly Breaks
AI doesn’t throw obvious errors; it breaks in subtler ways. A prompt becomes unreliable, the output subtly changes without any crash, user ghosting begins, ultimately leading to a collapse in confidence. Users won’t tell you they’re confused; they’ll just stop using the product. If you're not testing, logging, and monitoring your prompts, you're not shipping software. You're probably shipping vibes, and vibes don’t scale.
The Role of Conversation
Chat interfaces look great in demos but often confuse real users. In some flows, you need language, while with others, buttons are faster. Your job isn't to build a conversational chatbot; it’s to help users get to their goal with as little friction as possible.
Trust Starts with Recovery
Sometimes LLMs get things wrong. It’s not a bug; it’s part of how the system works. The question is: what happens next? Great products fail gracefully. They show their work, offer fallbacks, and let users recover quickly.
Trust isn’t achieved by building the perfect prompt but by helping users move forward even when things go wrong.
Checklist: This Will Hurt (In a Good Way)
Use this checklist to evaluate every AI feature before you ship:
- Does it solve a real user problem, or just look good in a pitch deck?
- Would a user actually pay for this?
- Can you explain how it works?
- Will it still work after a month?
- Can users steer, undo, or debug outcomes?
- Would you trust it in your own Ops stack?
If you’re unsure about any one of the above questions, take a step back. It’s better to fix it now than rebuild later.
This Is a Platform Shift. Treat It That Way.
AI isn’t just an upgrade; it’s a structural change. Think back to the early App Store era, where everyone’s shipping flashlight apps. The real hits came later. You’re not late; you’re early. Being early is a strength, which means you have time to slow down, focus on what matters, and build trust.
Final Word
Use AI, but wisely. Default to clarity and trust. And when in doubt, ship less, test more. The best products won’t shout about AI. They’ll quietly win by working better because of it. You’re not building a demo. You’re building a product. So don’t over-AI it.