From Data to Understanding: The Work Machines Can’t Do Without Tech Writers

Technical writers live in a strange middle ground. We translate what systems produce into something people can understand and use. Lately, that middle ground has gotten more crowded. Data teams, AI teams, and platform teams increasingly want documentation to behave like data—structured, atomized, and machine-friendly.

The assumption is that machines thrive on raw inputs and that humans can be served later by whatever the system generates.

That assumption is wrong!

The uncomfortable truth is this: data without context doesn’t just fail people—it fails machines too. The failure simply shows up later, downstream, disguised as “AI output.”

Most documentation problems trace back to a quiet but persistent confusion between four related — but very different — concepts: data, content, information, and knowledge.

They are often used interchangeably in meetings and strategy decks, as if they were synonyms. They are not.

Each represents a different stage in the creation, interpretation, and application of meaning. When those distinctions blur, teams ship docs that look complete but fail in practice (leaving both users and AI systems to guess at what was never clearly expressed).

A simple example, repurposed from Rahel Anne Bailie’s self-paced workshop for the Conversational Design Institute, Mastering Content Structure for LLMs, makes this painfully clear.

A Data Example

Imagine encountering the number 242.

On its own, it is nothing more than a value. Humans can’t reliably interpret it, and neither can an AI system. It could be a temperature, an identifier, a page number, or something else entirely. There is no intent encoded in it. No audience implied. No action suggested. It is easy to store and transmit, but useless for understanding.

This is the first misconception worth correcting: raw data is not inherently meaningful to machines. Large language models do not reason over naked values. Without context, they guess. And guessing is not intelligence.

Now add a little structure. Write it as +1 242.

Suddenly, recognition kicks in. A human reader may suspect it relates to a phone number. A machine may successfully classify it as a telephone-number pattern. That is progress—but only a little. Recognition is not understanding. Neither the person nor the system yet knows what to do with it.

This is where many organizations stop and declare victory. They call it “content,” ship it, and expect AI to take it from there.

But the real transformation happens when context deepens.

When you explain that the Bahamas participates in the North American Numbering Plan (NANP), that “+” indicates international dialing notation, that “1” is the shared NANP calling code, and that “242” is the area code assigned to the Bahamas, something important changes. The number is no longer just recognizable—it becomes usable.

Different readers understand different things. Someone calling from Europe understands they must use the international dialing format. Someone in the United States understands they dial 1 242 plus the local number. Someone inside the Bahamas understands they do not need to dial the country or area code at all.

A system, given this same context, can now generate accurate, audience-appropriate guidance instead of generic advice.

This is information. And it is the minimum viable input for both humans and AI systems to behave intelligently.

Here is where tech writers need to push back (firmly) on a common refrain: “We’ll just treat the docs as data.”

Data professionals are not villains here. They are optimizing for scale, governance, and automation. From their perspective, the content looks unruly. It resists schemas. It contains nuance. It varies by audience. So the impulse is to strip it down, atomize it, vectorize it, and let models infer meaning later.

But meaning is not something you can recover after you’ve removed it.