# Source: https://gitbook.com/docs/guides/seo-and-llm-optimization/geo-guide-how-to-optimize-your-docs-for-ai-search-and-llm-ingestion.md

# GEO guide: How to optimize your docs for AI search and LLM ingestion

Have you optimized your documentation for AI?

Modern teams are looking for ways to make their docs AI‑ready — and the answer is generative engine optimization (GEO).

By the end of this guide you’ll understand the best practices to optimize your docs for AI. So your product is explained — and cited — by AI assistants with the accuracy you expect.

### What is generative engine optimization (GEO)?

Generative engine optimization, or GEO, is the practice of structuring and writing documentation in a way easy for LLMs to ingest it reliably and cite it accurately.

And it’s super important — because users are increasingly asking AI first, and assistants prefer sources with clear structure, provenance and permissions. If you want your content to be cited by tools like ChatGPT, Perplexity, and Google AI Overviews, GEO is the answer.

Compared with SEO, GEO focuses less on ranking and more on machine readability, source attribution and answerability.

#### Generative engine optimization (GEO) vs. answer engine optimization (AEO): what’s the difference?

You’ve probably come across two similar terms in this space: generative engine optimization (GEO) and answer engine optimization (AEO). So, what’s the difference?

The short answer: there isn’t one. They’re interchangeable terms for the same concept.

At the moment, GEO/AEO is an emerging practice that’s evolving rapidly alongside the AI tools shaping it. Because the field is still so new, there’s no established industry standard — and no universally agreed name.

In this article, we’ll use **GEO**, simply because it’s the more common term right now — at least according to Google Trends. But until the industry settles on a single definition, expect to see both terms used interchangeably.

### Improve GEO: Quick tips

Like with SEO, there are a few rules to follow when building AI-optimized documentation. Let’s start with some content advice.

#### Content creation tips

* **Write atomic pages with one clear intent** – That means you should keep each page focused on a single concept, task or API area so it chunks cleanly during LLM ingestion.
* **Use descriptive H1, H2 and H3 headings** – Predictable anchors improve in‑answer deep links, so the AI tool can point users straight to the relevant part of your docs.
* **Use plain language, not marketing copy** – Much like technical users, LLMs look for semantics. So avoid figurative language and focus on writing precise, direct docs.
* **Add alt text and clear captions** – Multimodal models parse these fields to add extra context or to understand an image’s content. This is also best practice for accessibility, so you’re probably doing this already.
* **Place examples close to concepts** – Include short code blocks and request/response pairs near the explanation to make it easier for LLMs to understand. Again, this is just good docs practice so this is an easy win.
* **Define terms once and link consistently** – Consistently linking to relevant pages helps the AI understand common terms and their relationship to the current content.

#### Page design and site settings

* K**eep URLs stable and human‑readable** – Changing slugs can break embeddings and historical references. Make sure you follow URL best practices!
* **Enable a site‑wide sitemap and** [**llms.txt/llms-full.txt**](https://www.gitbook.com/blog/what-is-llms-txt) – These files help LLM crawlers discover canonical sources and allowed paths.
* **Use structured metadata** – Things like titles, descriptions, `robots` directives, and Open Graph tags. These signals improve crawl quality.
* **Avoid interstitials or gated assets for public docs** – Crawlers struggle with paywalls and script‑gated content. If you want your docs cited, make them easy to access for everyone.

### GEO best practices

There are three essential things to remember when optimization documentation for AI:

1. LLMs value well written and well structured documentation.
2. Don’t write your docs for AI. Write for your users and you’ll be rewarded with good GEO.
3. Creating helpful, accurate and up-to-date content will be rewarded by AI answer engines (and search engines)

With that in mind, the best approach to GEO is to treat it as part of your existing documentation workflow, not a one‑time checklist.&#x20;

Here are a few things to focus on when creating AI-ready docs. And it’s no surprise that they’re also similar to general documentation best practices:

#### Audit your page titles and descriptions

* Check that every page on your docs site has a title and description. This metadata is extremely important to help LLMs (and traditional search engines) understand the page’s content.

#### Structure your page effectively

* Add descriptive subheads that answer common user questions. It makes them more useful and scannable for both users and LLMs.
* Keep sections on your page to a maximum of 200–400 words. This makes it easier for LLMs to chunk your content.

#### Optimize code snippets and examples

* Provide minimal, runnable snippets with known inputs and outputs. Include language tags and titles for code blocks.&#x20;
* Pair conceptual docs with reference tables and constraints; LLMs use both during tool selection.

#### Follow image best practices

* Always include alt text and concise captions when you add an image. And make sure they describe the intent, not just the appearance.
* When possible, lean towards text‑based formats (such as Markdown or JSON) rather than screenshots so content is easier to index.

#### Answer common user questions directly

* Identify the most common questions users ask AI about your product. Then create pages that directly answer each with clear steps and descriptive titles.
* If you can, make the page heading a question — such as “How to rotate an API key” — to align with user prompts and search queries. Bonus: these pages are great for SEO, too.

#### Be consistent

* Make sure you keep brand and product names consistent, and avoid variant spellings.

#### Include attribution&#x20;

* Add metadata for the page’s author and last‑updated. This signals freshness and ownership, which are valued by LLMs.

### GEO in GitBook

[GitBook](https://www.gitbook.com/) makes it easy to implement an AI documentation strategy. As well as [automatically optimizing pages for SEO](https://app.gitbook.com/s/Ua3kTfM3iWAoECzM0u90/published-documentation/publishing/how-does-gitbook-handle-seo), GitBook automatically makes your docs LLM-friendly with some handy features.

What GitBook optimizes automatically for LLM ingestion:

* **Semantic structure** – GitBook uses clean HTML, [Markdown formatting](https://app.gitbook.com/s/NkEGS7hzeqa35sMXQZ4X/creating-content/formatting/markdown) for heading hierarchy, and code block metadata by default.
* **LLM-friendly formatting** – [GitBook auto‑generates LLM-friendly elements](https://app.gitbook.com/s/NkEGS7hzeqa35sMXQZ4X/publishing-documentation/llm-ready-docs) like sitemaps, `llms.txt` and `llms-full.txt` files, and MCP servers. Plus, it offers Markdown versions of every page, and creates stable, readable URLs to guide AI crawlers to canonical content.
* **Performance and render fidelity** – Fast, server‑rendered pages reduce crawl errors and ensure the text LLMs see matches what users see.

In other words: if you work with GitBook, you’re building on a foundation designed for AI‑optimized documentation — not just bolting GEO on later. GitBook is built to produce docs designed for humans, and optimized for AI assistants.

### How to test GEO for your docs

These tips should help you improve your discoverability by AI assistants. But how can you track whether your efforts have been successful?&#x20;

Here are a few tips to test your documentation GEO:

* Ask multiple AI tools targeted questions about your product’s features and workflows — then look for direct citations of specific pages and anchors.
  * Ask specific questions like “How do I create an access token for the API?” or “Give me step-by-step instructions for installing an extension”&#x20;
* Track referral traffic from AI tools and monitor which pages are cited most often — then improve any content that doesn’t get citations.
  * Tip: GitBook includes built-in analytics that help you track this kind of data — and many other insights.

{% hint style="info" %}

## Remember: results may take time

Models and search engines need to re-crawl your site before GEO changes show up in answers. This should happen fairly quickly, but don’t despair if you don’t see instant results.
{% endhint %}

### Wrap up

GEO is still an emerging standard, and while AI platforms haven’t yet shared any official information about how to optimize your content, we already have a good idea about what works well. Most important of all is writing great content. Combine that approach with the tips above and you should see great results in LLMs.

And if you’re looking for AI‑optimized documentation that reads beautifully for humans and performs for LLMs, [GitBook](https://www.gitbook.com/) is the best platform to get you there.&#x20;

Want to see why teams like Nvidia, Zoom and Amazon trust GitBook for their documentation? [Sign up today](https://app.gitbook.com/join) and start publishing docs that assistants love to cite.

—

<details>

<summary>Glossary</summary>

* alt text — text descriptions of images for accessibility and machine understanding.
* anchor link — a stable, deep link to a heading or section in a page.
* canonical URL — the preferred URL for a page to avoid duplicates and consolidate signals.
* content chunking — structuring text in small, titled sections that are easy for retrieval systems to index.
* generative engine optimization tools — utilities and platform features that improve crawlability, chunking, and grounding.
* generative engine optimization for documentation — practices that make doc sets LLM‑ready without sacrificing readability.
* grounding — the process of anchoring a model’s output to cited sources.
* llms.txt / llms-full.txt — lists guide AI crawlers through the structure and content of a documentation site.
* provenance — metadata that explains source, author, and update history for trust.
* retrieval‑augmented generation (RAG) — using retrieved documents to ground an LLM’s answer.
* schema/metadata — structured descriptors (title, description, robots) that aid discovery and ranking.
* AI‑optimized documentation / LLM‑ready documentation — content designed for retrieval‑augmented generation and accurate citation.
* AI‑friendly documentation workflows — authoring and review practices that preserve structure and provenance over time.

</details>