Essay · AI · Design Practice
Designing with AI: From Illusion to Intention
On what large language models actually are, why that matters for how we design with them, and the move from blind trust to genuine, informed collaboration.
01 / The Question
Do we actually know what this thing is doing?
A few months ago, I gave a talk to Harver's Product and Engineering teams. I didn't pitch a framework or walk through a case study. I just wanted to ask a question we don't ask enough in rooms full of people building with AI.
Not in a conspiratorial way. In a genuinely curious one.
01 / The Question
We work with AI the way people once worked with electricity.
We know it works. We've learned not to touch certain things. But the inner life of it? Still pretty mysterious.
And that mystery, I've found, is where a lot of well-intentioned design goes wrong.
01 / The Question
Apple called it an illusion of thinking.
Apple published research on reasoning-enabled Large Reasoning Models — the ones marketed as capable of "deeper thinking" — and found something uncomfortable: as tasks got more complex, these systems made more errors, not fewer.
The question that started the talk
"Do we actually know what this thing is doing?"
Asked to a room of designers, PMs, and engineers building with AI every day.
We use it. We rely on it. We rarely understand it well enough to question it.
02 / The Research
The placebo nobody talks about.
A CHI 2024 study found that participants consistently believed AI had improved their performance — even when they'd been explicitly warned beforehand that it might make things worse.
The belief in AI's capability improved outcomes more than the system's actual performance.
02 / The Research
The belief improved outcomes. Not the tool.
Participants brought confidence to their work because they thought they had a powerful tool. That confidence changed how they showed up. And the results reflected that — not the tool's actual capability.
This isn't a story about AI being good or bad. It's a story about how much our expectations shape what we experience.
02 / The Research
We're not just designing interfaces.
We're shaping belief.
We're shaping what people believe is possible. And that belief has real consequences — for how they work, how they trust, and how they act on outputs.
The design responsibility
"We're not just designing interfaces. We're shaping what people believe is possible."
And that belief has real consequences for how people work, trust, and act.
03 / What It Actually Is
It isn't reasoning
the way you reason.
When you type a message into an LLM, it gets broken into tokens — numerical representations of words and parts of words. The model processes those tokens through layers of mathematical calculations, finding the most statistically likely next token based on everything it was trained on.
It does this again. And again. Until it has a response.
It's finding the nearest
related vectors.
The output can look like thinking. It can feel like thinking. But understanding what's actually happening under the hood changes how you work with it.
You stop waiting for the model to figure things out. You start designing the conditions under which it performs well.
04 / Prompting as Design Work
Prompt engineering is interaction design for intelligent systems.
When you write a prompt, you're not just asking a question. You're designing a reasoning pattern. You're setting up the conditions for a particular kind of output.
The same instincts that make you a good designer make you good at this too.
04 / Prompting as Design Work
Three techniques that changed how I work.
Meta-prompting — asking the model to think about how it should approach a problem before it tries to solve it. It's like briefing a collaborator before they dive in.
Function encapsulation — breaking complex tasks into smaller, well-defined steps. Don't ask it to do everything at once.
Output templating — giving the model a structure to fill rather than a blank canvas. It performs better, and you get outputs that are actually usable.
04 / Prompting as Design Work
None of this requires code.
It requires design instincts.
Clarity about the goal. Empathy for what the system can and can't do. Iteration based on what you observe.
The skills are already there. The practice is new.
Clarity · Empathy · Iteration — the same skills that make a good designer make a good prompter
05 / The Shift
05 / What Changes
When we treat AI as magic, we abdicate something.
We stop questioning. We stop iterating. We accept outputs that we should push back on. We build products that reflect the model's limitations instead of our users' needs.
And the people using those products pay the cost — in decisions made on flawed outputs, in systems they can't interrogate, in trust that gets broken without explanation.
When we treat AI as math, we engage differently.
We ask better questions. We stay in the driver's seat. We design the conditions under which the model performs well, rather than hoping it figures things out on its own.
That's a more honest relationship with the tool. And it produces better work.
06 / The Intention
Not trust that's blind.
Not skepticism that's paralyzing.
But genuine, informed, curious collaboration.
The move from illusion to intention isn't about being skeptical of AI. It's about being thoughtful with it. Learning to think with it rather than surrendering to it.
Where you know what the tool is, what it isn't, and what that means for the work you're doing together. That's what good design with AI looks like.
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