This content originally appeared on DEV Community and was authored by Anindya Obi
AI promised to cut dev time. But in reality, we were spending more time editing half-finished code, cleaning up misaligned components, and fixing silent failures. The issue wasn’t AI it was how we were using it.
Step 1: Feed AI More Than Just Prompts
When AI starts from nothing, the results are unpredictable. Most tools ignore the actual structure, logic, and product flow.
Solution: Connect your specs, designs, and documentation up front. With context from Notion, Figma, and user flows, AI stops guessing and starts building with direction.
Step 2: Modularize Your Prompts by Task
Throwaway prompts waste time. We used to rephrase the same requests daily.
Solution: Create a set of prompt templates tied to common tasks, like “generate profile settings screen with editable fields” or “create 3-step form with error handling.” The more specific the prompt, the more
reliable the output.
Step 3: Add Lightweight Validation Checks
Fixing small logic gaps over and over again gets frustrating.
Solution: Set up auto-checks that catch missing state updates, broken handlers, or mismatched parameters. Even a simple lint layer can flag inconsistencies early.
The Result
We stopped treating AI like a magical code generator and instead gave it structure. Now, the outputs are predictable, aligned with our stack, and way easier to ship.
What We Built
This process became the backbone of HuTouch. It reads your specs, maps Figma components, and outputs structured Flutter screens you can actually use.
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This content originally appeared on DEV Community and was authored by Anindya Obi