Writing for AI: Start now, because AI’s already reading your docs

This article continues the conversation from “From AI-enabled to AI-native: Why technical writers must lead the next wave.” If you haven’t read that one yet, start there.

Let’s talk about the most urgent change technical writers need to make today: writing for AI. That’s the call to action. Simple to say, but transformational in practice.

For decades, we’ve written documentation with a human user persona in mind. We’ve carefully shaped our tone, language, and structure to align with empathy-based principles, guiding readers, anticipating emotions, and reducing friction. That approach no longer scales.

In an AI-native world, your user is no longer just a human. AI is fast becoming the primary consumer of your documentation.        

Here’s the shift:

• You write for AI. That’s the focus and scope of authoring.
• AI transforms and customizes your content. That’s the focus and scope of delivery.

This isn’t some hypothetical future. This shift is already happening.

If your leadership has sent emails about “AI-first,” “embedding AI in offerings,” or “transforming with AI,” then this message is for you. The shift isn’t coming. It’s here.

And if you haven’t received that email yet? Start preparing anyway, because your users are already asking the likes of ChatGPT, Copilot, and Gemini about your products. Will they find something they can actually use?

This transition isn’t just a mindset shift. It demands a serious overhaul of how we work.

We’ll dive into ontology management, knowledge maps, semantic tagging, terminology databases, and structured content systems in future posts.

Today, we focus on how we write. Start making an impact today (through better writing). Infrastructure will catch up.

We’ve inherited habits from the print era. We carried them through PDFs and polished them for the responsive web. Now, some of those once-sacred practices are holding us back.

It’s time to challenge our technical writing practices.        

So, let’s talk about the writing rules that need a refresh. These are not theoretical best practices. These are the hard-learned rules used every day in AI-first documentation teams. These seemingly small changes in the way we write can have massive effects on how well AI understands, repurposes, and delivers our content.

20 writing practices to make your documentation AI-ready

Below are 20 writing practices you can start changing today to make your documentation understandable, usable, and extensible by AI.

1. Avoid inline links

AI often strips formatting or reads content as plain text. Inline links can get lost or break sentence structure. AI benefits from clean semantic separation between content and navigation.

2. Flatten lists and avoid multilevel bullets

AI models tend to lose structure in deeply nested lists. Flattening lists improves comprehension and reuse.

3. Avoid icons in sentences

AI cannot "see" icons. Sentences with icons become incomplete or nonsensical when the visual element is removed in plain-text extraction.

4. Avoid vague visual references

AI cannot interpret layout or page structure. Don’t use phrases like “as shown above,” “in the image to the right,” or “see below.” These references become meaningless when the doc is consumed as plain text or extracted content.

5. Use terms consistently

Synonyms confuse AI. "Sign in" vs "log in" vs "authenticate" might mean the same to humans, but not to machines. Especially so when training on your docs.

6. Standardize spelling and terminology across variants

Pick a language variant (such as US English) and use it consistently. The same applies to product and technical terms.

7. Avoid contractions

Contractions can reduce clarity and make tokenization harder for AI models. Use full words instead of contractions like “don’t,” “can’t,” or “you’ll.”

8. Avoid pronouns and ambiguous references in steps

AI needs unambiguous references. Pronouns often refer to multiple possible antecedents, making it difficult for models to extract accurate instructions. Use the noun or subject explicitly in each step instead of relying on “it,” “them,” or “this.”

9. Use numbering only for ordered actions

AI assumes numbered steps are sequential. Using them for unordered items misleads the model and users alike.

10. Avoid using nonstandard numbering or lettered steps

AI expects numbered steps to use Arabic numerals. Avoid Roman numerals, letters, or unusual formats.

11. Keep your lists structurally consistent

Irregular phrasing in list items disrupts pattern recognition. Use the same syntactic structure for all items in a list.

12. Use tables for structured or comparative data

Tables are easier for AI to extract and index. They help with classification, comparison, and entity recognition. Use tables for structured or comparative data. Merging cells, adding steps, notes, and putting procedures inside cells must be avoided.

13. Avoid parentheses in instructions

AI often ignores or mishandles parenthetical content, especially when it appears in steps or structured content. Avoid inserting optional info or asides in parentheses. Use full sentences or dedicated notes.

14. Avoid emphasis-only formatting

AI may read content as plain text, stripping out formatting. Meaning should never depend on styling alone. Do not rely on bold, italic, or color styling to convey meaning. Always explain or label the purpose.

15. Use sentence-style in headings

Follow sentence-style capitalization to better pattern recognition, cleaner indexing, and alignment with natural language processing patterns.

16. Use semantic headings correctly

AI models rely on heading structures to parse topics, subtopics, and procedures. Misused headings make content look flat or disorganized.

17. Keep one idea per sentence

AI performs better when each sentence represents a discrete unit of meaning or action. It improves chunking, indexing, and reuse in AI-based systems.

18. Avoid embedding critical steps in notes or tips

AI may ignore or downgrade content inside elements like notes and tips. Keep essential steps in the main text flow.

19. Modularize your content for reuse

Write self-contained chunks that can be reused across contexts. AI tools perform better with modular, component-based content.

20. Label and tag content semantically

Use metadata, semantic tags, and structured labels to help AI interpret meaning and context more accurately.

Final thoughts

Shifting to AI-native documentation is not a sprint. It’s a multi-quarter transformation that touches every part of your documentation ecosystem. But it starts with writing.

The checklist above may look basic, but its impact is foundational. By aligning your content with how AI parses, chunks, and learns, you're setting yourself up for scalable reuse, conversational delivery, smart search, and accurate knowledge graph creation.

Here’s what to do next:

• Start updating your internal style guides with these rules.
• Train your writers and editors to spot and fix these patterns.
• Normalize your terminology and labeling.
• Start writing AI-readable documentation.

When your company is ready to deliver AI-first experiences, your documentation will be a strategic enabler.

Start writing for AI now. Because AI is already trying to read what you wrote yesterday.        

#TechnicalWriting #TechWriting #TechComm #TechnicalCommunication #STCIndia

How many of these practices do you agree with?

How many are already part of your writing today?

And how many will you commit to fixing by the end of this year?

Drop your thoughts in the comments. Let's make writing for AI a collective upgrade.