Checklist: How to structure content for LLMs

Most websites experienced a steady traffic decline since AI answers and ChatGPT rolled out, but that doesn’t mean your content isn’t being used anymore. In fact, it’s probably being used more than ever, but now it’s just a source for an answer instead of the whole answer.

SEO traffic may be declining, but our content is being retrieved by ChatGPT constantly to generate answers - covering all the different topics that used to get organic search clicks.

Weekly citations by different LLM bots for a specific URL.

When people do research for purchasing decisions in ChatGPT, you want your brand to show up. It’s not so much about website traffic anymore, because the research happens within ChatGPT, (or Gemini/Perplexity) - but rather about the right information being retrieved and shown to users when they are actively looking.

This matters because traffic from LLMs seems to convert at a much higher rate than traffic from search, indicating that a lot of the research has already happened before someone ever visits your website.

So how can you make sure that your content is used by LLMs to generate answers relevant to your brand, and that it uses the information you want it to use and show?

#Are we in the age of writing for machines again?

With GEO, it sometimes feels like it’s 2005 again, when keyword stuffing, cloaking, and link spam were effective tactics to rank. Marketers were writing for search engines rather than for the people who would actually read the content.

The tactics have changed since, for example:

• Now it’s about getting mentioned in every listicle and Reddit thread instead of linkspam.
• Tools like salespeak.ai feed ChatGPT an optimized version of your page that real users don’t see.

But one thing stays the same: While this kind of tactic might lead to a temporary uplift, long-term visibility requires proper content structure.

Feeding the LLMs content in a format that they can easily digest doesn’t contradict writing it in a way that’s also easy to digest by actual people. But while we’re writing for humans, there are still certain factors to consider to show LLMs that your content is worth being used for its outputs.

Structuring content for LLMs is also not the same as writing for SEO, even though there are many parallels, like using a clear hierarchy of headings. But with AI search positions within RAG answers and the overall sentiment of those answers becoming much more important, you want to ensure you structure content in a way that’s easily retrievable and will be cited accurately.

Visibility, sentiment, and position are all tracked separately

#What changed: From “ranking pages” to “retrieving passages”

Search engines index pages/URLs, and then rank them as answers for a specific query.

But AI search works differently: The AI runs your prompt, pulls snippets, then generates answers and (maybe) cites the sources/URLs.

So instead of optimizing a specific page, you are optimizing extractable blocks of meaning.

How LLMs actually “read” your content and generate an answer

1. Crawl/fetch: collect the source content (web, docs, DB).
2. Parse/normalize: turn it into clean text + metadata (titles, sections, URLs, permissions).
3. Chunk (ingestion): split into retrievable units (often with overlap/structure).
   Embed + index: create vectors for each chunk and store them for search.
4. Query prep: rewrite/expand the user question; add filters (time, permissions).
5. Retrieve: pull the most relevant chunks
6. Context pack: trim/merge chunks to fit the prompt; attach chunk IDs for citing.
7. Generate answer: LLM reads only the packed context + question and writes the response.
8. Cite: map claims to the provided chunk IDs/links.

So the model doesn’t use your whole page when generating an answer, but just retrieves the most relevant chunks.

It means where you split the text into chunks determines what information gets pulled in as context for the model to use when it generates an answer.

#How content structure influences AI-search visibility

So how should you structure your answers so that your content is most likely to be used in AI generated answers? There are three relevant factors.

The content must be:

1. Parseable
2. Chunkable
3. Citable

1) Parseability: Bad parsing limits retrieval

Structuring content for parseability means using clean HTML/Markdown that usually extracts cleanly.

Layout-heavy PDFs with several columns on the other hand, are a known problem. The same applies to slides or images where the layout conveys the meaning.

2) Chunkability: Each chunk should stand on its own

Retrieval often returns excerpts, not the full page, so you have to write in a way that any excerpt still works:

Write chunks to be “standalone” instead of relying on “as mentioned above/below”, because retrieval may return only that paragraph and miss the earlier/later context.

Put the main answer first, with the important conditions. State what to do right away, then immediately add key details like limits & requirements, so a short excerpt still makes sense.

Use identifiers and synonyms: For example, include the exact UI path, feature name, error code, and common aliases in the same chunk, so the excerpt still matches queries and is clear even without the rest of the page.

3) Citability: Give the model something easy to quote

The easiest content to cite tends to be:

• definition-first
• structured lists
• tables for parameters/constraints
• Q&A blocks / FAQs

A simple framework to use is the “inverted pyramid”:

1. Lead : the answer in 1–2 sentences (what it is / what to do), plus key identifiers
2. Key details: steps, constraints, examples, common edge cases.
3. Background: explanations, rationale, extra context, links, history.

Now that you know what LLMs are looking for, here is a checklist on how to structure content on your site in a way that actually gets cited, and gives LLMs the information you want it to show.

#10 Tips to structure your content for LLMs

The following section is meant as a checklist for better ai-readable content structure.

1. Write in self-contained, chunkable sections

Just like with SEO it’s important to use a clear heading hierarchy (H1 → H2 → H3), one topic per section. Avoid phrases like “as mentioned above” because retrieval might not include the “above.”

A headless CMS you can provide these sections: With Hygraph you can model self-contained “units” as components and join them via relations so each unit can be retrieved independently.

2. Relate claims and insights to product features and your brand in each relevant paragraph

You don’t just want your content to be cited, but you want those citations to improve your brand visibility. That’s why you need to tie as many claims and insights to your brand as possible and highlight how specific features can solve certain problems. (Like the Hygraph examples above)

It might feel like you’re repeating yourself, but since the LLMs view every chunk of content separately, you increase your chances of your brand or product being mentioned by an LLM when connecting it to a specific chunk. More details on this in this talk by HubSpot.

3. Make formatting easily machine-parsable

• Short paragraphs, bullets, code blocks.
• Consistent patterns like: Problem → Cause → Fix → Example.

Yes, in a way this is exactly the style that ChatGPT writes itself.

You might have read that AI generated content doesn’t rank in Google. But the evidence here is quite mixed, with some arguing for and against that case.

My hypothesis is that whether AI generated content is valued by search engines and answer engines, is not about the structure (that’s usually very clean), but rather about offering anything new, i.e. information gain.

4. Add retrieval-friendly “handles”

• Use query-shaped headings (“How to rotate API keys” > “Key rotation”).
• Include explicit entities and synonyms near the answer (product name, feature name, common aliases).

5. Use FAQs the right way

FAQs are one of the key snippets that are often used for AI-answers. But oftentimes they read like someone just guessed what people might ask, or even repeat what was already said in the body of the page. AI-generated FAQ sections are especially guilty of that.

Instead, use actual customer questions. We extract them from Gong transcripts and collect them in Slack with a simple n8n workflow:

Then we can just query ChatGPT (Slack access has to be enabled of course) to get actual questions for any topic:

This is an easy way to add FAQs to each page that are relevant and unique.

6. Use Tables: Turn facts into structured objects

Tables often beat prose when it comes to being cited.

For example:

7. Use small, complete content blocks

Make your text easy for AI to understand by breaking it into small, complete blocks:

• Keep related ideas together. If you explain a rule and its exception, put them in the same section.
• Use short sections. Aim for about half a page (around 500–600 words) per section, unless the topic truly needs more.
• Add mini-headings inside sections. A few small headers help both people and AI quickly see what each part is about (like in this list).

8. Use structured metadata

Structured metadata (like schema.org JSON-LD) doesn’t create visibility on its own, but it can make it much easier for systems to understand, index, and confidently reuse your content.

For example:

• Organization + WebSite
• TechArticle for docs pages
• FAQPage for troubleshooting
• HowTo for step-by-step tasks
• BreadcrumbList
• SoftwareApplication

#Checklist Summary

1. Write in self-contained, chunkable sections
2. Relate claims and insights to product features and your brand in each relevant paragraph
3. Make formatting easily machine-parsable
4. Add retrieval-friendly “handles”
5. Use FAQ sections based on customer insights
6. Use Tables: Turn facts into structured objects
7. Use small, complete content blocks
8. Use structured metadata

#The CMS layer matters more than ever

If LLMs retrieve chunks instead of ranking pages, your CMS becomes the foundation of your AI visibility.

Hygraph gives you the structure LLMs need: clean schema management, structured metadata, canonical handling, and full hreflang support, all delivered through stable APIs. That means your content isn’t just crawlable but also retrievable, reusable, and citable in AI-generated answers.

If you want to win in AI search, you need a CMS built for it. Get in touch today to see how Hygraph can help you with LLM visibility.