There’s logic behind your gut feeling
Charles Peirce named it abduction. UX design and AI prompting depend on it.
Everyone says, “Trust your gut.”
No one explains how your gut thinks.
Charles Peirce did.
He called it abduction — the logic of the best guess. The leap from idea to hypothesis. The kind of thinking that doesn’t guarantee you’re right, but gets you close enough to try.
In the late 1800s, Peirce (pronounced “purse”) laid out a model for how humans form beliefs. Not by waiting for divine insight or following perfect rules, but by starting with uncertainty, getting uncomfortable with what we think we know, and forming a guess worth testing.
Though his work, he coined the term inquiry. It’s the kind of logic that shows up today in early product strategy, design research, and speculative AI prompting. But most people have never heard of it. Or him.
The scientist who studied doubt
Peirce was a logician, mathematician, physicist, and among many other things, a founding mind of American pragmatism. But his real obsession was how we form beliefs… and what it takes to change them.
He believed that truth wasn’t something handed down. It was something you worked toward. And the way you got there wasn’t by doubling down… it was by being willing to be wrong.
For Peirce, doubt wasn’t a weakness. It was the start of real thought.
He outlined three basic ways humans reason:
- Abduction: What might be true, based on an informed guess.
- Deduction: What must be true, if certain rules hold.
- Induction: What’s probably true, based on patterns we’ve seen.
Abduction
Peirce believed abduction was the starting point of thought. The origin of all insight. It’s how new ideas enter the room. It’s what lets a product team hypothesize why a metric dropped. What lets a designer anticipate confusion before it happens. What lets a researcher frame the right question — not just analyze the data.
Abduction is the leap from observation to possibility. From “what’s going on here? to “maybe it’s this.” It doesn’t promise you’re right, but it give you something you can test.
Deduction
Deduction traces back to Aristotle. It starts with general truths and reasons from there.
If all humans are mortal, and Nate is human, then Nate is mortal.
Very simple. No guesswork.
It’s the logic of systems, policies, and automation. It’s how engineers ensure that code behaves and how compliance teams catch violations. Deduction is how we reason with what’s already known. But deduction can’t generate new insights. It can only validate what fits the rules. The way I like to think about it is that it builds from what is, not what could be.
Induction
Induction, also rooted in Aristotle but expanded by thinkers like Francis Bacon and David Hume, works in the opposite direction. It looks at what’s observed and infers what’s likely.
The sun has risen every day so far, therefore, it will probably rise tomorrow.
It’s the logic behind science, analytics, and machine learning. It finds patterns, spots trends, and flags probabilities. Induction tells teams what users did, but not necessarily why or what to do next.
It’s powerful, but it’s all in hindsight. It’s a retrospective. Without abduction to frame a question, and deduction to apply constraints, induction just collects data. It watches, but it doesn’t exactly know what to look for.
The UX of belief
Peirce didn’t just study how we reason. He studied how we believe, and why bad beliefs are so hard to shake. In his 1877 paper The Fixation of Belief, he laid out four common ways people decide what’s true:
- Tenacity: I believe this because I always have.
- Authority: I believe this because someone powerful told me.
- A Priori: I believe this because it feels intuitively right.
- Scientific Method: I believe this because it holds up under scrutiny.
Only one of these welcomes doubt. The others reward comfort. They feel true because they’ve always felt true. Possibly because someone credible said them (anchoring), or because they’re easy to repeat and hard to question.
Belief doesn’t always come from evidence. It comes from emotion, repetition, and sometimes, even convenience.
That’s why people stay in toxic jobs and subscribe to conspiracy theories. It’s why entire teams build around an idea that felt right in the kickoff meeting and then never gets revisited again.
Peirce’s warning wasn’t just philosophical. It’s very practical:
If you skip the discomfort of doubt, you also skip the chance to learn.
Designing with abduction
Most modern design work starts with a hunch. You notice something. You guess why it’s happening. You test that guess.
That’s abduction.
You’re not proving what must be true or confirming what’s probably true. You’re asking: What might be going on here?
This is the core of discovery research and journey mapping. Real human-centered design. It’s the kind of work that sits with ambiguity long enough to learn something from it, not the kind that lives in a slide deck acting like it already knows.
The problem?
We don’t teach abduction as a skill. We treat it like intuition. Something senior designers “just kind of know.”
I think Peirce would disagree. To him, abduction wasn’t magic, it was logic. Abduction is trainable, explainable, and it’s also something worth practicing.
If deduction is the logic of math, and induction is the logic of science, then abduction is the logic of design.
It’s how good designers ask better questions… before anyone chases answers. And it’s why they aren’t just building. They’re noticing.
Abductive reasoning through GenAI
Abduction doesn’t stop at design. It shows up any time we try to make sense of something unclear. And nowhere is that more relevant (or more overlooked) than in how we use generative AI.
Before GenAI, deep thought was slow. You had to sit with ambiguity, make sense of scattered information, and challenge your own assumptions. It hasn’t been efficient, but it worked.
Now, with GenAI, we have a tool that can simulate new perspectives, surface counterarguments, and help us test ideas faster than ever before.
But most aren’t using it that way. They come in with an outcome in mind, not a question. They’re looking for confirmation, not exploration. They treat the tool like a vending machine.
When used well, prompting is abductive. You start with a hypothesis, frame the question and explore what might be true. Then refine it in real time.
When used poorly, the tool reflects your first assumption. It sounds convincing, but it doesn’t deepen your understanding.
Peirce warned about this long before language models existed. When belief is accepted too quickly, it stops evolving. When questioning disappears from the process, so does learning.
Prompting is a chance to practice abductive reasoning, but only if we use it with the same care we once gave to thinking on our own.
So what?
We’re not designing for truth. We’re designing for beliefs that can evolve and that means getting comfortable with ambiguity, asking better questions, and resisting the urge to rush toward certainty.
If AI is going to help us think (not just generate outcomes), then we have to teach people how to speculate with care and test assumptions. We also have to teach people how to change their minds without losing their grip.
Charles Peirce gave us a model to help us reason when the facts aren’t all there. A method for thinking that is flexible and willing to be wrong.
It still holds up.
And if you’re interested how those habits of thought get built in the first place, I wrote separately about Friedrich Froebel, the educator who showed us how to shape how we learn.
We spend a lot of time talking about how to train AI. But we rarely ask how we train ourselves to think.
Peirce did.
And it might be time we listened.
There’s logic behind your gut feeling was originally published in UX Collective on Medium, where people are continuing the conversation by highlighting and responding to this story.
This post first appeared on Read More