Writing for Both Humans and Language Models
By Maksym Bardakh Ivko · Chief Executive Officer
In short
Content now has two audiences: human readers and the language models that retrieve and summarize it. These audiences want largely the same things. Clear claims, explicit structure, defined terms, and accurate framing serve both, because a model reasons better over content a person finds clear. Writing for machines means applying the discipline good writing already demands, consistently. Keyword stuffing and stilted prose help neither audience.
Two audiences, mostly aligned
Content used to be written for people, with search engines as an intermediary to satisfy. Now language models read content directly to answer questions, which adds a second audience. The reassuring part is that the two audiences want largely the same things. A model extracts and reasons over content more reliably when that content is clear, well-structured, and unambiguous, which is exactly what helps a human reader. Writing well for people is most of writing well for machines.
This means the temptation to write a separate, machine-pleasing version, padded with keywords or contorted to game a system, is misguided. It tends to produce worse content for humans without reliably helping machines, which increasingly value the same clarity people do.
What serves both
The qualities that serve both audiences are the ones good writing has always valued, now with higher stakes. State claims clearly and directly. Structure content so its organization is explicit, with headings that signal what each part contains. Define terms rather than assuming them. Keep statements accurate and avoid framing that misleads. Each of these helps a reader follow the content and helps a model interpret and represent it correctly.
- Make claims clear, direct, and accurate.
- Structure content explicitly so both readers and models can follow its organization.
- Define terms and state subjects plainly instead of relying on assumed context.
Why keyword stuffing fails
Writing for language models is sometimes misread as a return to keyword stuffing or to mechanical, stilted phrasing aimed at machines. Models trained on language reward content that reads as competent and clear, and penalize the artificial patterns that older tactics relied on. The discipline is to write accurate, well-organized content and trust that it serves both the person reading and the model answering.
Key takeaways
- Content now has two real audiences: human readers and the language models that read it.
- The two audiences mostly want the same thing: clear, structured, unambiguous content.
- Clarity, explicit structure, defined terms, and accuracy serve both at once.
- Writing a separate machine-pleasing version tends to harm humans without helping machines.
- Writing for models is the discipline good writing already demands.
Frequently asked questions
- Do humans and language models want different things from content?
- Mostly no. Both are served by clear claims, explicit structure, defined terms, and accuracy, because a model reasons better over content a person finds clear.
- Should I write a separate version for AI?
- No. A separate machine-pleasing version padded with keywords tends to produce worse content for humans without reliably helping machines, which value the same clarity.
- Is writing for language models the same as keyword stuffing?
- No. Models reward content that reads as competent and clear and penalize the artificial patterns older keyword tactics relied on.
References
About the author
Maksym Bardakh Ivko
Chief Executive Officer
Maksym leads product at BBMM Technologies. He designs Flowo’s scheduler and writes most of what ships in it.