How AI is creating a flywheel for Design
Exploring product concepts used to take weeks to months. At Sequence, we now prototype dozens of concepts in a single day. Using AI, we can now systematically explore a larger solution space than was previously economical.
This is not about coming up with solutions, but about forming a mental model that feels right, faster.
Most product teams end up:
- Choosing safer, more predictable features over experimental ones
- Debating concepts in Google Docs rather than building real prototypes
- Building the first reasonable idea instead of finding the best one
Our process looks like this:
1) Write the experimental prompt
Instead of jumping into Figma, we start by writing a detailed prompt that captures:
- The core user problem we're solving.
- The mental model we want to test (as opposed to specific UI).
- Key interactions and user flows.
- Relevant domain context. Often a one-pager, PRD summary, or customer feedback to give the AI context about our product and the problem space we’re in.
This prompt becomes our experimental hypothesis.
2) Generate 10+ variations
Using Claude Artifacts, ChatGPT, Lovable and Magic Patterns, we generate 10+ different prototypes using variations of the same original prompt.
We remix prompts by forcing the LLM to approach the problem from different perspectives (e.g. "redesign this as if you were Notion,"). The goal is to get creative and generate concepts beyond conventional thinking. Each generation explores different solution paths we might have previously explored manually.
3) Rapid evaluation and synthesis
Next, we assemble all prototypes on a canvas, highlighting what works and what doesn't. Then we synthesize the best elements into a new prompt and generate another round. This way, we can quickly form a mental model of what works and what doesn’t.
4) Rapid customer feedback
Once we've converged on a promising direction, we can immediately share a link with customers.
This way ideas can get tested quickly, and customer feedback influences the concept while it's still malleable.
Low-fidelity prototypes work better
We intentionally keep these concepts unbranded and visually rough. Generic fonts, basic colors, placeholder content. Magic Patterns even has a ‘Wireframe’ mode that forces prototypes to stay simple.
Overly polished prototypes create an anchoring bias. It’s easy to fall in love with specific visual implementations rather than focusing on whether the underlying concept actually solves the user's problem.
The goal is to answer "does this solve my problem?" rather than "how does this look?".
Most products fail because they are built on a wrong mental model of the user
In other words, the product model doesn’t fit the user and domain model in the real world. Users reject features not because they're poorly designed, but because the underlying mental model doesn't match how they think about the problem.
By rapidly prototyping a large variety of concepts, we can build a stronger intuition for what model is the best fit for a given problem we’re solving.
August 2025