AI coding tools generate functional but often generic designs. The key to creating a beautiful, personalized application is for the human to act as a creative director. This involves rejecting default outputs, finding specific aesthetic inspirations, and guiding the AI to implement a curated human vision.
A powerful, non-obvious use for LLMs is information restructuring. By feeding a standard online recipe to ChatGPT, you can ask it to reformat the instructions so that ingredient measurements appear directly within each step. This eliminates scrolling back and forth, making recipes easier to follow.
To get precise results from AI coding tools, use established design and development language. Prompting for a "multi-select" for dietary restrictions is far more effective than vaguely asking to "add preferences," as it dictates the specific UI component to be built and avoids ambiguity.
When using "vibe-coding" tools, feed changes one at a time, such as typography, then a header image, then a specific feature. A single, long list of desired changes can confuse the AI and lead to poor results. This step-by-step process of iteration and refinement yields a better final product.
AI code generation tools can fail to fix visual bugs like text clipping or improper spacing, even with direct prompts. These tools are powerful assistants for rapid development, but users must be prepared to dive into the generated code to manually fix issues the AI cannot resolve on its own.
Instead of accepting default AI designs, proactively source superior design elements. Use pre-vetted Google Font combinations for typography and find specific MidJourney 'style reference' codes on social platforms like X to generate unique, high-quality images that match your desired aesthetic.