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Rob Hanna and Lance CummingsGood communication has always been about understanding your audience — shaping your message to match how they think, what they need, and what they’ll do with it.
Information typing, the “IT” in the technical documentation standard DITA, codifies that practice into a design discipline that organizes content around its function rather than its form.
Rob Hanna and Lance Cummings explore how information typing clarifies writing for human readers and have discovered in the process that those same structural principles help AI deliver better results.
We talked about:
- their mutual interest in information typing, the “IT” in the DITA technical documentation standard
- the origins of information typing in textbook-building research at Stanford by Robert Horn in the 1960s
- how aligning information by function and to human capabilities reduces cognitive load
- how Lance’s early work trying to teach structured thinking about content to his students led to his study of information typing
- information typing as a design discipline
- the origins of structured authoring well before DITA was created
- the rhetorical nature of an information typing system
- the three foundational info types in DITA – concept, task, and reference – and two extensions that Rob has developed — principle and process
- the importance of the process information type in agentic AI architectures
- the role of rhetoric, in particular the application of “machine rhetorics,” in establishing context for AI agents
- the importance of being aware of the connections between how our brains work and what content is
- some examples of research they’ve done that shows how structured content improves results in AI systems like RAG (Retrieval Augmented Generation)
- Rob’s advice to technical writers to become well-versed in structured authoring to adapt to the need to write for both humans and machines
- Lance’s assertion that “information typing isn’t just for technical writers”
Rob’s bio
Rob Hanna is the CEO and co-founder of Precision Content. For more than 30 years, he’s helped organizations turn complex documentation into structured, reusable knowledge that supports people, processes, and increasingly, AI. His passion is improving outcomes for companies embarking on structured authoring journeys—helping them avoid costly missteps, align teams around a practical content strategy, and build systems that make information easier to create, govern, find, and reuse. At Precision Content, he leads teams of writers, information architects, trainers, and developers to raise the standard of technical communication through DITA, CCMS implementations, metadata, microcontent, and information architecture. He’s taught structured authoring, metadata, and taxonomies, and he’s a Fellow of the Society for Technical Communication. What drives him most is seeing organizations move from fragmented content to reliable content supply chains that deliver better experiences for customers, employees, partners, and digital systems.
Connect with Rob online
Lance’s bio
Lance Cummings is a professor of English in the Professional Writing program at the University of North Carolina Wilmington. Dr. Cummings explores content strategy and writing in technologically and culturally diverse contexts both in his research and teaching. His most recent work looks at how to leverage structured content with rhetorical strategies to improve the performance of generative AI technologies and shares his explorations in his newsletter, Cyborgs Writing .
Connect with Lance online
Video
Here’s the video version of our conversation:
Podcast intro transcript
This is the Content Strategy Insights podcast, episode number 215. When you’re trying to communicate with either human beings or computers, it helps to align the kind of information you’re imparting with the way the recipient expects to receive it. Technical writing experts call this “information typing,” the practice of organizing content around its intent, not its format, which better aligns the information to human cognitive preferences. It turns out that this practice also helps AI systems deliver better results.
Interview transcript
Larry:
Hi, everyone. Welcome to episode number 215 of the Content Strategy Insights podcast. I am really delighted today to welcome to the show Rob Hanna and Lance Cummings. Rob is the CEO and the founder at Precision Content, a technical content consulting company. And Lance is a professor of professional writing at University of North Carolina at Wilmington. He’s also a well-known content creator. We’ll talk a little bit more about that. So welcome to both of you, and maybe start with Rob. Rob, tell the folks a little bit more about what you’re up to these days.
Rob:
Perfect. Thanks, Larry. Yes, we’ve been pioneering a writing methodology that we believe makes content easier for people to use. And as it turns out, easier for machines to use. So, there’s continuous development and research in this field on how we bring these together and create better future-proof content. So, I’ve been embroiled in that for the last number of years.
Larry:
Nice. And Lance?
Lance:
Yeah, so I’ve been teaching and researching how we write and collaborate in diverse linguistic and technological environments. And I’ve been writing about AI since before ChatGPT in online, and kind of found my niche with technical writing. I was introduced to DITA probably back in 2017, ’18, something like that at a conference and been trying to figure it out ever since. And really, ran into Rob’s stuff at a conference, and the information typing clicked for me. Whereas I understand DITA and the use cases, but haven’t necessarily had the time to really dig in and use it on my own, or to teach a class on it. But the information typing really got me thinking about structured writing in a different way, especially in concerns with AI, which I’ve been exploring on my blog, Cyborgs Writing on Substack. Been looking at structured approaches, how you take structured approaches, combine that with rhetoric to make AI work better. And I’ve really found information typing to be a pretty big part of that recently, and been working on developing approaches in AI writing systems and things like that.
Larry:
Nice. And you’re reminding me of the reason this conversation came together. One of you, I think… I can’t remember which of you posted it, but you had jointly created this document called DITA Information Types Aren’t Templates, which I think is one of the key concepts behind DITA. I wonder maybe, Rob, maybe you can talk about that a little bit. What led to that article and a little bit about the difference between types and templates?
Rob:
Sure. Perfect. Thank you. Yeah, this has been a crusade of mine for a very, very long time: understanding what information typing is. I like to say it’s at the heart of DITA. And D-I-T-A Information Typing, it’s right there, but there’s so little discussed or talked about what information typing actually is. In my work with the OASIS subcommittee, Business Documents Subcommittee, back in the aughts, we were looking at how we extend beyond product support and into the rest of the business. So, I studied a lot of different models that we could look to for extending DITA beyond simply concept, task, and reference, and landed upon information mapping. And so, that’s where I discovered the genesis of information typing as defined by Robert Horn back in the late ’60s at Stanford University, where he came up with these ideas around these information types based on patterns that he had found in textbooks.
Rob:
His task was to try to find a methodology that would help to build a better textbook. And so, through his analysis of a lot of text, he found these patterns in that content. And from there, a number of other folks in pedagogy and other areas started to look at why do these patterns emerge. And so, where we’ve landed with that is that that information types represent the function of information. In other words, how do our brains work with that information? How do we lower cognitive load for the reader by aligning information by function, and not mixing content together that have different purposes, different functions, different outcomes?
Rob:
So we can sort that apart, tease them apart, and signal to the reader what type of information it is that they’re looking for. We can dramatically reduce the cognitive load and improve performance on that content. So, it’s well beyond what a template was ever designed to do. This is more a template for your brain. This is how your brain works and how we template the information. Going here and actually do something with that information.
Larry:
Yeah. As you said that, it’s a higher level than just WYSIWYG thinking. It’s like, what are you doing here? What’s the function versus the form that it takes in its expression? And one of the things that makes it so powerful, I think, is its alignment with how humans think. Maybe you can talk a little bit about this, Lance, is how the cognitive science that supports information typing and a lot of other DITA stuff as well.
Lance:
Yeah. So, I’ll start by saying one of my hobbies has been to take the idea behind DITA and try to apply it to my education materials that I use with students, and also my own notes structures and things like that, because I see the value of reuse and modular thinking. And that’s one of the things that I did in my blog when AI first came out, was to try to apply those. But a lot of times, trying to use the DITA labels, they didn’t quite fit and I had a lot of trouble. And sometimes, you just make up your own labels and things like that, and really tried to teach that structured thinking to students in that way, which with some mild success. But then when I ran into information typing, it just all clicked.
Lance:
And I think it’s because it matches those ways of thinking that we’re already intuitively following but we don’t always explicitly talk about. And I would say that’s one job of a rhetoric teacher, or a writing teacher, or even a literature teacher is to make explicit the templates that we’re using when we’re writing. So there’s a study, I can’t cite it off the top of my head, but of literature classes where they find that literature teachers are expecting certain templates when they’re grading papers, but they don’t necessarily make those templates explicit.
Lance:
So, they listed… So, they figured out what some of these templates are and then taught them to students, and then the grades went up. So, it’s really the same thing. I really connect this back with my background in rhetorical theory all the way back to Aristotle where he’s talking about what he calls topoi. And literally, it means little rooms in your head. So, they’re like patterns that speakers can access when they have to give an impromptu speech, because that was what you did back there. Back then, you didn’t write blogs to convince people, “You had to go up and make speeches.” And you’d be like, “Okay, this argument needs a process argument, this argument needs a comparison argument, and you could construct a speech just like that.” And that’s really followed us through the history of communication, rhetoric, and writing. Back in the 19th century, there was the modes of discourse, which comparison, definition, process…
Lance:
So, I already had an idea of these information types already, but hadn’t quite connected them to structure quite the way that Robert Horn had in his research in the ’60s and ’70s. And I think that’s why… And I found teaching students, when I introduce it in this more methodical way, it clicks better for them because it’s more visible and explicit.
Larry:
I got to say, I’m loving… I don’t know that either of you knew this before this call, but my first career before I got into digital content was in academic textbook publishing, and we thought a lot about this from that angle. And I love that some of the original research, like you were saying, Rob, that goes into this, comes from a guy who trying to write better textbooks. And then what you’re saying, Lance, applying these principles to your educational materials, I love this alignment.
Larry:
But you’ve both mentioned in your last few minutes this notion of patterns. And when you start to articulate patterns of stuff that happens, you end up with something that looks like a design discipline. Is that a good way to think about info typing as a design discipline?
Rob:
Yeah, absolutely. It’s not simply a metadata label will flap on a piece of content. We need to be able to telegraph to the reader what type of information this is based on how we title this block, this topic, this chunk of content. What sort of structures we provide to this information, so that visually they can look at something and they can get a signal as to what type of information this is. And then how we write it: second, third person, active voice. Whatever the information type requires, we want to be consistent in how we’re writing and structuring that information, ensuring that the content is concise, it’s well-structured, and it’s relevant for the type of information that it’s trying to convey.
Larry:
I think I pulled a quote from that, I think your paper, “Our brains work according to defined functions that aid in comprehension and application of information.” That’s what you just said, basically. But can you talk a little bit about… Because I think a lot of people… Because the way CMSs work and things like that, that a lot of people conflate structure and the intent of a thing.
Rob:
Absolutely.
Larry:
And this teases that out. Can you talk a little bit about… Either or both of you, about how that teasing out and being clear in your mind helps communicate ideas better.
Rob:
If you don’t mind, I’ll start on this one. Robert Horn was one of the first to coin the term structured authoring. And if you go on to Google Scholar and you look for Robert Horn, you’ll find some of his earliest works where he structured content using a typewriter. It looks terrible. But all of this, to say is that structured authoring preexisted before XML did. So, structuring content is not about the tooling you use or the format behind the content that you’re using. It has everything to do with using repeatable patterns and language. And that’s really what it is, is creating these patterns that I recognize. And while the DITA tag set may be somewhat verbose and complicated, our brains naturally acclimate to these patterns if we use them regularly and very, very quickly. So once I see a pattern and I understand that that’s a task, I’m going to see another task. It’s being telegraphed before I even read the words what it is, and I know how to handle that. I know what part of my brain I need to turn on.
Rob:
I love what Lance was talking about, rooms in your head, because we believe that there’s, on the cognitive science side of things, is that these rooms represent different types of memory that we need to access to provide context to information as we’re reading it, as we’re working with it, as we’re using it. So it’s really is about, “How do I activate the right part of the brain to be able to work with this piece of information most effectively?”
Larry:
Nice. And some of you said – Oh, I was going to say. Lance, I was going to ask you. Just say what you were going to say because I had a question as well.
Lance:
Oh, okay. I was just going to say the idea of design, I think one of the things that attracted me to the information typing system is that it is more than just structure, and it is rhetorical in the sense that you are trying to put the intent into the structure, into the piece that you’re creating, and that requires intentional design of the content. Not just labeling it like a certain kind. And now with AI, it’s more important than ever, because if that intent’s not there, then the AI just guesses what the intent is or just forgets about it.
Larry:
I’d love to… At this point, because we’ve alluded to and talked a little bit about the types. And there’s three kind of main types in DITA, and then there’s extensions where you can add more. And just the fact that they align so well with your rhetorical understanding and approach to things and the needs in technical communication. Maybe Rob, maybe you can talk a little bit about just the overview of the basic types in DITA.
Rob:
Yeah, you bet. So as I said before, Robert Horn really pioneered this area, and he originally identified seven different types of information as he boiled the ocean of textbooks. Sorting information not by what it said, but how it said it. Those seven types were procedure, fact, process, concept, structure, and principle types of information. Now, the only word that’s familiar with DITA there is concept. There’s a concept in DITA, and there’s a concept in information mapping. But if you break it down further where a fact is reference and procedure is task, you have other information types that appear in Business Documents that are broken down in this model. And so with Precision Content, we’ve adopted concept, task, and reference, but we’ve introduced principle and process on being two additional archetypes from which we would create content for, and we would create specializations around if we need to extend those further.
Rob:
So, piece of principle information is really talking about advising the reader what to do, or not do, and when. So, this may be in the form of an admonition, a caution, a warning. It could be a tip. It could be a best practice. It could be anything that, like I say, is telling somebody, “Don’t do that,” or, “Do do that,” and when, like I say. So, there’s no real structure for that. We have notes in DITA. We’ll just pop a note into the middle of a task and say, “Don’t stare at the laser.” We’ve got a little bit more sophisticated admonitions for machinery, but by and large, there’s no topic type for that. So, we created a topic type for that and a writing methodology around principle. And process. Wow, process is the big missing archetype for DITA and technical communication right now, where we’re talking about how things work.
Rob:
When I say things, I mean, how does mail get delivered? How are my taxes processed? How does the printer work or how do planes fly? So, each one of these have actors. Each of these actors have actions that they perform. These identify accountabilities, and who’s responsible for what, and in what sort of process or what order do these get performed in to accomplish a particular outcome. We don’t see a lot of this described in technical documentation typically, because the processes are not well-defined on how would I use a piece of commercial software to do a thing. There’s no well-defined process on how that works, that naturally flows out of it. But when you’re looking at banking procedures or you’re looking at something that’s much larger, process starts to take on a much more important role.
Rob:
And when we look to agentic AI, agentic AI is really looking for process information far more than it’s looking for task-based information, because it needs to understand what are the outcomes and what are the types of things that need to happen in order to reach that particular outcome. So anyway, I’m big on process these days. They really help to unlock and make it easier to get into context for tasks that need to be performed. But again, they’re going to have a huge impact, I think, on the agentic AI side.
Larry:
Yeah. No, I can totally see that. Lance, I’m curious now about how well all of what Rob just said aligns with your rhetorical approach to things or your rhetorical background. But also, you’ve done a lot with AI, and a lot of experimentation and stuff. And maybe, how the… especially the principles, but I think maybe all of the types, how they contribute to this notion of context that everybody has become so much more aware of with the arrival of AI?
Lance:
Yeah. So really, what rhetoric comes down to is… The traditional word definition is the available means of persuasion. But really, it has many different definitions now, depending on what you want to do with it. But really, it’s about shaping the world to make things… Get people to do things that you want them to do, or to convince them to see the world in a certain way. Machine rhetorics is what I call it. Convincing the AI or giving the AI the vision to actually do what it is that you want it to do. And so, being able to lay out the actual different kinds of information like that really makes it… Especially just like if you were to give it to a student, for example. A lot of times, our worst assignments are because we don’t make those things explicit. We give them the tasks but not the process, or we give them the process but we don’t give them the concepts they need to know to do the process, understand the process, that kind of thing.
Lance:
And it’s really the same thing with agents, or with the documentation that those agents create. And I’ve really found that it increases – the AIs perform better. Oh, it helps you really expand on your documentation, too. So for example, I’ve built documentation for a study abroad bot, for example, which can run on my notes, which are just random scattered things. It can pick and pull things, and then guess at what I might need in an answer. But if I go through my notes and say, “Okay, this is actually a process. Let me actually describe that as a process in more detail.” And then that gives you questions that somebody might ask about that process, and then you can add things to that that you was missing because somebody asked that question. Or you can have the AI be like, “Okay, what questions might somebody ask about this topic?”
Lance:
Then you can add those things to it, and then it really helps you expand the documentation. Not just categorize everything, but realize what’s missing, what’s mislabeled. And that really improves what the AI has available for its outputs, not just as a way of making the outputs better, but more complete and accurate.
Larry:
Rob, I see you nodding vigorously. I wonder if you have anything you want to add to that.
Rob:
I think we lose the connection between how our brains work and what content is. Content comes from our brains. Our brains shape these patterns. These patterns show up in content. They’re maybe not visible when it’s right in front of you, but when you step back to 50,000 feet, you see these patterns, just like Robert Horn saw those patterns by pulling apart all of those textbooks. AI sees these patterns in our content that we don’t see up close. And that’s what makes AI able to better retrieve information by its general shape based on the human corpus of content that it has consumed.
Rob:
So, it’s really bringing content back to human cognition to machine cognition. Machines understand it well because they’re being fed off of our human corpus of content. That’s where the connection really lies: finding those patterns and exploiting those patterns.
Larry:
And that’s really interesting that I’ve read a number of articles recently on the differences in how machines do cognition and how humans do. There’s this famous… The epistemological disconnect between human and computer language abilities, and I’ve seen others as well. But it sounds like… And Lance, I know you’ve seen evidence of this, and you’ve both seen evidence of how structuring content in this way and making that meaning explicit… Not only making the meaning explicit, but structuring it consistently.
Is there any science or studies that show X percent better retrieval or understanding? Question-answering ability or things like that with structured content? Either of you, yeah.
Rob:
Okay. We’ve done some experimentation with Retrieval Augmented Generation or RAG on taking our content and breaking it down into these chunks, and then looking at how we use RAG with the metadata layer, and with the content broken down into what we call micro content pieces, and looking at the frequency in which it returns accurate results. So, there’s very small scale test. We don’t have a large corpus of content to test against, but our very earliest experimentation is showing very, very positive results for improved retrievability of that content. But I think Lance has a more interesting story to tell around experimentation in this area.
Lance:
Yeah. So I think in short, there isn’t really any research, at least that I’ve found. Maybe it’s out there, and somebody can send it to me, but that specifically addresses this. There are some research done on chunking, and RAG, and things like that that are tangential to this… But don’t focus on actual information types, partly because a lot of those studies are done in computer engineering, and so they have a particular mindset when they’re approaching those projects. And so, one of the goals that Rob and I have is to do this study in a way that we can replicate it and share it with the rest of the industry. And as part of that, I’ve been working on a grant in my university with a class, with my writing with AI class, but we’re also working with a computer engineering class on a disaster communications chatbot.
Lance:
So we’re in Wilmington, we have hurricanes and stuff, disaster communication’s a big thing. And what we did was we had students… We created a taxonomy last semester at some of my colleagues of topics that would be in a chatbot. We adapted that and then we separated those topics among all the students, and their goal was to create system prompts together along and then to feed the AI the knowledge through their own RAG systems. We actually used open source models with LLM Studio, and then to run tests to see how it works. Now, I had asked my English students to do two versions of the knowledge. The normal way that people are doing this, and especially in computer engineering, is going to the website and stripping it with Firecrawl or whatever, or uploading the PDFs. And then I was asking my English students, “Okay, take a couple of these key pieces and break it down into information, rewrite it as a topic with the information types in there, and then add things in it that you think would be important for your UNC-W audience.” So, it becomes more customized, and also more structured and organized.
Lance:
Not all of my students did it. It was a very hard project to do, but not all my students did it. But I ended up with a group of chatbots that had the structured knowledge in a group that didn’t. And there was a pretty big difference with how they performed. Some chatbots were providing wrong phone numbers, that they put in the UNCW websites, for example. Or they weren’t appropriately giving context for rules about feeding infants, things like that. But the ones with the structured knowledge way outperformed the other ones. We used Perplexity to basically judge the answers according to human answers and using specific test questions that the students came up with. It’s not particularly a scientific study, it was a classroom project, but I think it’s something we could replicate. And it was very interesting to see those differences.
Lance:
And then the students who did structure their knowledge, seeing their eyes light up when they realize what they’re seeing, which is a really amazing thing and something that hope to continue doing with Rob.
Larry:
Yeah. No, what you both just said, I also spend a lot of time in the knowledge graph world in the symbolic AI space, and there is a lot of evidence there that supports the notion of ontologically structured knowledge graphs just way outperform. And you both have done experiments that are on the path to further validating that.
Larry:
But hey, I can’t believe it. We’re coming up close to time already. But before we wrap up, I want to make sure I give each of you a chance. Is there anything last, anything you want to revisit from the conversation, or that you just want to make sure we share before we wrap up? Rob, I’ll let you go first.
Rob:
It’s through looking at how are we going to create content that is going to improve machine and human performance. So, studying that. If you’re not in structured authoring yet, you don’t know what structured authoring is, you’ve got to dig into this because I think it’s going to be highly, highly important to your careers as technical writers, technical communicators, to be able to be well-versed in how structured authoring works, and what sort of advantages you need to give your content so that it’ll work well for machines as well. So, I think we’re on the cusp of just not understanding structured authoring is just not going to be an option for technical writers going forward.
Larry:
Nice. Lance?
Lance:
I would certainly agree with that. Also, the ability to see these patterns. I’ve found our English students, for example, or writers in general, are really good at seeing these patterns, maybe not necessarily articulating them, which the information typing gives us the verbs and the organization to say the labels to just use to describe these, especially for AI. But I would say that information typing isn’t just for technical writers, and that’s probably the case I’ll be making on my Substack Cyborg’s writing that certainly learning about this is, I think, going to be necessary for technical writers and content designers and all of that, people working with AI. But I think it’s useful for everybody to think about. Most people will be working with AI probably in the workplace. But even just thinking about how you structure your email, how you structure your syllabus or your assignment, it’s pretty useful in all those areas, and is kind of the case I’m making in my classes.
Lance:
And honestly, if I teach it in an introduction to professional writing, I actually believe those students will learn more about writing and content than if they just were writing the traditional essays that we’re always assigning in those classes, or doing reports and emails and things like that. Being able to think about structure and use it in creative ways is really, I think, where we’re at for, at least, professional writing in my opinion.
Larry:
No, as you were both talking, it occurs to me that the language is the interface now to everything. So we’re all writing all the time and it behooves all of us to develop these skills. Hey, one very last thing, what’s the best way to connect with each of you? Rob?
Rob:
The best way to connect with me is to reach out to me on LinkedIn. So, I’m happy to make connections. I’m publishing there multiple times per week. I’m providing links into my Substack. I’m only just getting started with my articles there. But I’d say the best way to reach me is by LinkedIn. And then from there, you can DM me, or send me an email, or anything you want. So, look for Rob Hanna. I’m also known as the single sorcerer on LinkedIn. So, do a search and happy to connect.
Larry:
Great. Lance?
Lance:
So, I’m on LinkedIn a lot. That’s where I post the most. And then I, of course, got my Substack. It’s called Cyborgs Writing, www.isophist.com. That’s where I’ll be writing a lot about using information typing, and AI, and content, things like that in writing. And text writing.
Larry:
Cool. Thanks. And I’ll put all of that in the show notes as well, of course. Thank you both so much. I really enjoyed the conversation.
Rob:
Thank you. It was great.
Lance:
Thank you. It was fun.
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