From Data to Understanding: The Work Machines Can’t Do Without Tech Writers
AI hallucinates because the unstructured content it processes lacks the context required for meaningful, reliable interpretation — context that tech writers can provide
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.
