Unsorted content gets chunked arbitrarily by AI, and the structure you built vanishes the moment it’s uploaded. This free guide covers the five information types and a decision procedure for typing any chunk, so your content stays legible to readers and AI alike.
We’ve all heard it by now ... garbage in, garbage out. But what does that exactly mean?
Give a model a pile of unsorted notes and it will impose its own order on them. The output can read confidently, but still not sync up with your context and knowledge.
Sorting your content by what it does before the model sees it is often a better fix than a prompt or model upgrade.
I’ve been talking about information types for a while (and using them to structure agent documentation). But I realized I haven’t shared an in-depth reference that shows exactly how I’m currently implementing them.
This is the guide I actually use. It covers the five types, the decision procedure, and the places everyone gets confused (including AI). It’s free, and there’s a PDF at the bottom you can just keep.
Here is the core rule: One chunk does one job for one reader need.
If a chunk seems to fit two types, it is usually two chunks. Split it before you type it.
I find that even AI has trouble with this. So I keep updating the reference in the agent documentation I write.
(Paid subscribers can keep up with updates in my public skills library.)
Where this comes from
This framework is sixty years old. In 1969, Robert Horn pointed out that content has a structure independent of its topic. How you organize a piece of information depends not on what it’s about, but on the job it does for a reader.
His method, Information Mapping, ran through technical writing and instructional design for decades. I’ve written about how Horn accidentally described how ChatGPT would work and what information actually is if you want the longer version.
Precision Content teaches this framework today, and I use Rob Hanna’s five types as the foundational guide for developing structured content and agent documentation.
What has been a discipline for writers turns out to be exactly what makes knowledge legible to an AI model.
The five types at a glance
Every chunk does one job for one reader need. Five types cover those jobs, and each one answers a different question.
The decision procedure
Run the tests in order and stop at the first Yes. The order matters because it catches the easy-to-identify forms first and leaves Concept as the default.
If two tests both say Yes, the chunk is mixed. Split it and type the pieces separately.
The confusing pairs
These four collisions cause most of the errors.
Process vs. Task ... who acts?
Ask whether the reader performs the steps. If you can rewrite each step as a command the reader executes (”Click Save,” “Enter your ID”), it is a Task.
If the steps only make sense in the third person (”The system validates the request,” “The bill moves to committee”), the reader is watching a flow they do not control. That is a Process.
“How the approval workflow moves” is Process; “How to submit a request for approval” is Task.
Concept vs. Principle ... describe or prescribe?
A Concept explains and you cannot violate it. A Principle guides action and you can follow or break it. The test is whether someone can disobey it.
For example, “A style guide defines house conventions” is a Concept (a description). “Use sentence case for headings” is a Principle (a rule you can obey or ignore).
Concept vs. Reference ... understand or retrieve?
Concept is prose you read once to grasp an idea. Reference is data you return to and scan for a specific value.
If it is a definition written to be understood, it is Concept. If it is a lookup entry, table row, or spec you would never read start to finish, it is Reference.
A paragraph explaining what an API key is → Concept. A table of key names, types, and defaults → Reference.
Principle vs. Task ... rule or steps?
A Principle states what should be true and is not sequenced. A Task is ordered and performable.
“Always back up before upgrading” is a Principle. “1. Export the database. 2. Run the upgrade. 3. Verify.” is a Task.
A Principle often explains why a Task step exists, but it is a separate chunk.
Signals to look for
Cues a person or a model can detect in the text itself. Treat them as evidence, not proof. Confirm with the decision procedure.
Worked examples
The decision procedure, applied to real chunks. Read each one, then check the type and the reasoning.
Common mistakes
Mixing description and instruction in one chunk. A paragraph that explains what something is and tells you how to do it is a Concept and a Task glued together. Split them.
Calling a Process a Task because it has numbered steps. Numbering isn’t enough. Ask whether the reader performs the steps or watches them happen.
Calling a Principle a Concept because it sounds explanatory. If the reader could obey or violate it, it is a Principle.
Describing the tool instead of naming the concept. The Concept slot pulls toward “this skill produces…” or “this report shows…”. That is purpose, which belongs in a description or the Task.
Concept names the thing the reader must understand to make sense of the rest. If the subject of the sentence is the tool itself, it is probably a description, not a Concept.
Burying Reference data inside prose. A specification, threshold, or list that readers will scan for one value belongs in a table or list typed as Reference, not in a sentence.
Typing a whole document as one type. Types apply to chunks, not pages. A single page usually contains several.
When in doubt
Ask what the reader is trying to do with the chunk:
understand it (Concept)
look something up (Reference)
decide or judge (Principle)
follow how something works (Process)
perform steps (Task)
The reader’s need settles most cases the surface features leave open, right?
Why this matters for AI
AI reads content in chunks, not documents.
When your content is typed, the retrieval system finds the right unit, and the answer is grounded in your actual work. When it isn’t, though, the system chunks arbitrarily, and the structure you built vanishes the moment it’s uploaded.
I’ve written about reading with information types and using information types to build and evaluate prompt structures if you want to see the method in practice.
There’s also a worked example of information typing on a real source.
The PDF
The same guide, formatted for printing or keeping on your desk. Download the Information Type Guide (PDF)
The agent-ready version
If you want the reference version formatted for AI systems to read, with the same decision procedure written so a model can apply it, it’s in my Structured Skills Library ... alongside the skills I use to build and maintain a typed knowledge base.
Good structure and clear purpose is key.
I think too many people focus on the wrong thing. A tech writer for instance is not responsible for generating AI searches, neither should he be. Even SEO/AEM experts have no clue how to drive AI searches. They have best practices they monetize. If they were paid on results, the whole profession would disappear.
At the end of the day, the doc is here to help your target audience understand whatever it is you're doing. This is hard enough!