The creative process with AI involves exploring many options, most of which are imperfect. This makes the collaboration a version control problem. Users need tools to easily branch, suggest, review, and merge ideas, much like developers use Git, to manage the AI's prolific but often flawed output.

With models like Gemini 3, the key skill is shifting from crafting hyper-specific, constrained prompts to making ambitious, multi-faceted requests. Users trained on older models tend to pare down their asks, but the latest AIs are 'pent up with creative capability' and yield better results from bigger challenges.

A powerful workflow is to explicitly instruct your AI to act as a collaborative thinking partner—asking questions and organizing thoughts—while strictly forbidding it from creating final artifacts. This separates the crucial thinking phase from the generative phase, leading to better outcomes.

Avoid using AI to create sales outreach from scratch ('black pen'). Instead, use it as an editor ('red pen'). Apply the 10-80-10 rule: 10% human-led prompting, 80% AI-driven task execution, and a final 10% human refinement. This maintains quality while boosting efficiency.

Users mistakenly evaluate AI tools based on the quality of the first output. However, since 90% of the work is iterative, the superior tool is the one that handles a high volume of refinement prompts most effectively, not the one with the best initial result.

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.

The most creative use of AI isn't a single-shot generation. It's a continuous feedback loop. Designers should treat AI outputs as intermediate "throughputs"—artifacts to be edited in traditional tools and then fed back into the AI model as new inputs. This iterative remixing process is where happy accidents and true innovation occur.

When a prompt yields poor results, use a meta-prompting technique. Feed the failing prompt back to the AI, describe the incorrect output, specify the desired outcome, and explicitly grant it permission to rewrite, add, or delete. The AI will then debug and improve its own instructions.

Leverage AI as an idea generator rather than a final execution tool. By prompting for multiple "vastly different" options—like hover effects—you can review a range of possibilities, select a promising direction, and then iterate, effectively using AI to explore your own taste.