If your AI-generated designs look generic, the problem probably isn't the tool — it's how you're talking to it. Here are the five most common mistakes we see designers make, and how to fix each one.
Mistake 1: Starting Without Context
The problem: Jumping straight into "Design a landing page for a SaaS product" without any setup.
The fix: Before your design request, provide a brief that includes: the brand personality, target audience, key differentiators, and visual references. Just like you'd brief a junior designer.
Mistake 2: Being Too Vague OR Too Specific
The problem: Either "make it look good" (too vague) or a 500-word specification that constrains the AI too much (too specific).
The fix: Aim for the goldilocks zone. Specify the important constraints (brand colors, layout structure, tone) but leave room for the AI to contribute creatively. Think creative brief, not technical specification.
Mistake 3: Ignoring Iteration
The problem: Generating one output, deciding it's not right, and starting over from scratch.
The fix: Treat AI outputs as first drafts. Use follow-up prompts to refine specific aspects: "Make the hero section more bold," "Reduce whitespace in the pricing grid," "Shift the color palette warmer." Build on what works.
Mistake 4: Not Using Visual References
The problem: Describing what you want entirely in words when you could show it.
The fix: Most modern AI tools accept image inputs. Upload mood boards, competitor examples, or your existing designs. A single reference image communicates more than a paragraph of description.
Mistake 5: Using the Same Approach for Every Tool
The problem: Copy-pasting the same prompt into Midjourney, DALL·E, and v0 and expecting comparable results.
The fix: Each tool has different strengths and responds to context differently. Midjourney loves aesthetic and mood descriptions. DALL·E excels with precise compositional instructions. v0 wants component specifications and design tokens. Learn the language of each tool.
The Underlying Principle
All five mistakes come down to one thing: treating AI tools like vending machines instead of creative collaborators. The better context you provide, the better output you get. It's that simple — and that hard.
This is why we built our Context Engineering course. It teaches you a systematic framework for providing the right context to any AI tool, every time.