Dev.to
7/14/2026
Building with AI: lessons from shipping an AI-native product in four months
Original: Lesson 0 - Learning to build with AI: where I learned not to trust it
Short summary
A 25-year engineering veteran shares lessons from building an AI-native procurement tool end-to-end using LLMs over four months. Key insights: Gemini struggled with unit test failures while Claude resolved them quickly, leading to a multi-model validation architecture. The real challenge isn't generating code — it's staying on top of what AI produces, as agents quietly drop requirements they deem unimportant.
- •Gemini failed at fixing its own unit tests; Claude resolved them in 30 minutes
- •Multi-model validation catches different errors across providers
- •AI's real risk is silently dropping requirements it judges unnecessary
Generated with AI, which can make mistakes.
Is this a good recommendation for you?
