This past week I was in a conversation with Scott Abel — if you don’t know him, he’s one of the most influential voices in technical communication and content strategy.
He mentioned something another thought leader in structured content had said to him: you don’t need to validate it. It just works. Do it.
Scott and I are both trying to build the empirical case, so that comment landed with a little friction. It’s been rattling around in my head ever since.
He’s not wrong. And my guess, he is in a position where he can say that and be believed.
Most of us aren’t.
Those of us in academic positions, or making the case to skeptical stakeholders or CEOs, have to do the harder thing: explain why it works.
And that explanation starts with a word we use constantly without ever pinning down what it means.
I’m Lance Cummings. And welcome to my (what now seems to be) monthly podcast that explores deep research on AI and writing.
We use “information” to mean everything. Context is information. Instructions are information. Facts, procedures, principles, background knowledge — all information. When everything is information, the word stops doing useful work
The word we’ve been misusing
Here’s the problem.
We use “information” to mean everything.
Context is information. Instructions are information. Facts, procedures, principles, background knowledge — all information.
When everything is information, the word stops doing useful work.
Maybe that vagueness has been manageable in content design, where the main cost is poor user experience.
It’s becoming a real problem as we try to build AI systems that go beyond generating text to building systems that persuade, explain, and instruct in ways that actually serve readers (also called rhetoric).
If we are going to make progress in this area, the we need to make clear the difference between data and information.
It’s a distinction the philosopher Luciano Floridi has been developing for twenty-five years. And it turns out rhetoric has been working on the same problem for about 2,500 ... we just use different words.
The three conditions that makes information
In his Information: A Very Short Introduction (Oxford, 2010), Floridi draws a hard line between data and semantic information.
Data is just stuff — symbols, signals, marks on a page.
Information is what data becomes when it clears three conditions:
It must be well-formed,
meaningful,
and true.
All three. Miss one and you still have data, regardless of how well-organized it is.
Here’s what that looks like concretely. Imagine someone hands you these facts about making pot roast:
A pot roast takes 3 hours. Brown the meat first. You need carrots, potatoes, and beef. Cook at 325 degrees. Season with salt, pepper, and rosemary. The meat should reach 145 degrees internally.
Every fact is accurate. But try to cook from it.
Do you season before or after browning? Do the carrots go in at the start or the end? When do you check the temperature?
The facts don’t tell you, because nothing in the list says what kind of thing each fact is.
Now take the same six facts and organize them by what they’re for:
What you need: beef, carrots, potatoes, salt, pepper, rosemary.
What you do first: brown the meat, then season.
How it cooks: 325 degrees for 3 hours.
How you know it’s done: internal temperature hits 145.
Same facts.
But the arrangement has added something that no individual sentence contained: a claim about what each piece is for.
Ingredients are what you gather before you start. Steps are what you do in sequence. Conditions are how you verify.
That categorical claim — this is the kind of thing this is — is what converts data into information you can act on.
Notice something: you probably recognized the second version as a recipe before you finished reading it.
The structure is a genre, or a pattern that signals “this is organized for use” before you’ve processed a single fact.
The format itself is information.
Most AI failures aren’t random. They cluster around these three conditions.
A hallucination fails the third condition: well-formed and meaningful, but not true.
A misunderstood instruction often fails the first: parseable but not well-formed for the specific task.
A prompt full of relevant content can fail the second: accurate facts that the model can’t construct meaning from because the context lacks coherence.
We’ve been treating these as “model” problems.
Floridi’s framework says they’re information quality problems, which means they have structural solutions, not just scale solutions.
Adding more content to your prompt doesn’t fix any of them.
Organization is meaning-making
Let me bring in a framework that might seem like a detour but isn’t.
Roman rhetoricians organized the work of communication into five areas called canons:
invention,
arrangement,
style,
memory, and
delivery.
I’ll be coming back to all five in a longer piece.
But here’s why I’m mentioning them now: the second canon which they called dispositio (or arrangement) was never understood as an organizational problem. It was understood as the act of making meaning.
We tend to teach arrangement as the organizational part of writing. Where does the thesis go? How do you sequence your argument?
You’re finding the best container for ideas you already have.
That’s not what Cicero meant, and it’s not what the tradition understood.
Arrangement in the classical sense is constitutive. It’s not how you deliver meaning. It’s how meaning comes into existence.
The same material arranged differently isn’t just harder or easier to follow. It does different cognitive work.
Think about the difference between a bibliography and an argument.
Same sources.
The bibliography is data — accurate, organized, findable. The argument is information, because the arrangement has done the work of converting accurate citations into something that means something to a specific reader in a specific situation.
Every student who’s turned in a paper with all the right sources but somehow no argument has experienced this from the receiving end.
This is exactly what technical writers do when they convert a subject-matter expert’s data dump into structured documentation.
They’re not organizing existing information. They’re creating information out of data.
And when that process fails ... when a document has all the right content but still doesn’t work, the failure is in the arrangement, because it hasn’t cleared Floridi’s second condition. It hasn’t made meaning.
So when someone with enough authority says “it just works” about structured content approaches, what they’re observing is that information typed by function creates meaning more reliably than data organized by topic.
That’s a dispositio argument.
Whether you’re building systems, designing course materials, or just trying to get a useful output from a prompt, structure is epistemic, not cosmetic. Organization creates knowledge. It doesn’t just make it look nice.
What this changes in practice
This should reframe how you diagnose document failure.
When something doesn’t work, the standard question is “can users find it?” Floridi adds two more: Is it well-formed for this specific task? Does the arrangement create meaning, or just accuracy?
Those are different problems with different solutions, and neither one gets fixed by adding more content.
For educators, the data/information distinction is a teaching tool.
Students produce data constantly. One might say, “accurate, researched, grammatically correct data” that hasn’t cleared the meaning condition.
The feedback “organize your ideas better” doesn’t tell them what’s wrong.
The feedback “your arrangement isn’t building meaning yet? What do you want the reader to understand before they reach your conclusion?” does.
That’s the difference between teaching organization and teaching dispositio.
Whether you’re building systems, designing course materials, or just trying to get a useful output from a prompt, structure is epistemic, not cosmetic. Organization creates knowledge. It doesn’t just make it look nice.
If the same facts arranged differently produce different information, then how you organize your inputs is a meaning-making act, not just a delivery mechanism.
This is exactly what my Writing with Machines course works through — how to apply these distinctions to prompt design, AI-assisted writing, and teaching with AI. Link in the show notes.
Where this is going
Dispositio is one thread in a longer argument I’m working toward.
The classical rhetorical canons are, I’m starting to think, a pre-modern framework for the exact problem Floridi is formalizing.
Each canon maps onto a different dimension of that same problem:
how you generate material in the first place,
how you arrange it for meaning,
how style does cognitive work,
how knowledge gets stored and retrieved.
Each one has direct implications for how we build AI systems that actually serve readers.
I’m developing that argument in a longer piece. Watch for it.
And if someone in your field is wrestling with how to make the theoretical case for structured content and information typing, share this episode .... because the pragmatists are right that it works, and the reason it works is 2,500 years old.
I’m Lance Cummings. Until next time — arrange your data like it means something.
Sources:
Luciano Floridi, Information: A Very Short Introduction (Oxford UP, 2010)
Classical sources on dispositio: Cicero, De Oratore; Quintilian, Institutio Oratoria
