LLM SEO: Get AI Crawled and Ranked in 2025

LLM SEO is the practice of optimizing your site for knowledge capture inside large language models. Unlike traditional SEO, which focuses on ranking in blue-link results, LLM SEO ensures your content is structured, accessible, and fact-dense enough to be absorbed, grounded, and reused by generative engines. In Google’s ecosystem, this means preparing content for Gemini-2.5-flash, which powers AI Overviews, and Gemini-2.5-pro, which powers AI Mode. These models don’t just crawl pages—they capture passages, entities, and structured data to rebuild knowledge graphs and generate answers. If your content isn’t optimized for this process, your expertise and brand authority risk being excluded from the AI-driven conversations that increasingly shape how users discover and trust information.

Key Takeaways

Technical SEO for LLMs

Technical SEO for LLMs is crucial because large language models are, at their core, engines of knowledge capture. They rely on crawling, indexing, and grounding mechanisms to absorb content and decide which passages, facts, and entities are worth reusing in generative answers. If your site isn’t technically accessible, properly structured, and machine-readable, LLMs can’t capture your knowledge. Meaning, your expertise, data, and brand authority are excluded from the very systems shaping how users discover and trust information.

Related: What is Generative Engine Optimization (GEO)?

Introduction: Key AI Crawlers to Know

Large language models (LLMs) like ChatGPT, Claude, and Perplexity rely on a growing ecosystem of specialized crawlers and user agents. Unlike Googlebot or Bingbot, these crawlers often serve dual purposes. Some gather training data, while others fetch live content in real time to cite in generative answers. For technical SEO, understanding which agents are hitting your site—and how to manage them—is critical.

Want to skip all of this? Have the Go Fish team of experts give you an AI Search audit.

Here are some of the top technical SEO challenges for LLMs and what you should do to resolve them:

1. Render-Blocking Websites (JavaScript Challenges)

One of the biggest technical SEO issues for LLMs is JavaScript-heavy websites that block or delay rendering. Here’s why:

2. Accessible URLs and Internal Linking Architecture

Another critical technical SEO issue for LLMs is making sure your URLs are accessible and well-structured in the page source.

3. Sitemaps and Timestamp Accuracy

Sitemaps are still a cornerstone for AI crawler discoverability, and overlooking them creates major visibility gaps.

4. Taxonomy and Site Hierarchy

For large-scale websites, taxonomy design isn’t just about navigation—it’s about how crawlers and LLMs reconstruct your site into a knowledge framework.

Related: GEO vs SEO: Differences, Similarities, and More

5. Robots.txt and AI Crawler Accessibility

A growing number of site owners are tempted to block AI crawlers in their robots.txt files, but this often does more harm than good.

6. Page Packet Size

When it comes to AI crawlers, page packet size plays a different role than it does in traditional SEO.

Related: AI Overviews SEO: Get Visibility in 2025

7. Optimizing Native Assets (Images, PDFs, and More)

AI search engines don’t just process text—they’re increasingly adept at handling multimodal content, including images, PDFs, and other native assets. This makes asset optimization a crucial part of technical SEO for LLMs.

8. The Use of llms.txt Files

The llms.txt file is a newer, experimental standard designed to give LLMs a curated overview of your site. Unlike XML-based files such as sitemaps, llms.txt is written in Markdown, with sections that can describe your project, link to important resources, and highlight which files are most relevant to language models. It’s located at /llms.txt in your site’s root directory.

9. Auditing for Structured Feeds

Structured data is one of the most reliable ways to help both traditional search engines and LLMs understand, ground, and reuse your content. For AI Overviews, AI Mode, and other generative search features, structured feeds ensure that crawlers can interpret entities, relationships, and key details at scale.

On-Page SEO for LLMs

When LLM crawlers finish capturing a site, the next stage is ranking and selection. Unlike traditional search engines, LLM-driven systems evaluate content through a generative pipeline. This involves expanding queries (query fan-out), testing semantic relevance, grounding against trusted sources, and prioritizing freshness before presenting an answer to the user.

Here are some ways to optimize your pages for LLMs:

1. Structuring Pages for Knowledge Capture

For LLMs, not all pages are created equal—content needs to be structured in ways that make knowledge capture simple and extractable.

2. Auditing for Semantic Matching Signals

At the foundation of LLM retrieval is semantic matching—the process of connecting user queries (and their fan-out variations) to the closest aligned content. While advanced, these systems still lean heavily on basic semantic cues that live in your site’s metadata and markup.

3. Optimizing for Recency Signals

LLMs weigh freshness heavily when deciding what content to surface. Pages that clearly communicate when they were created, updated, or fact-checked give AI crawlers confidence that the information is current and reliable.

4. Expanding Fact-Density with Unique Insights

LLMs prioritize fact-rich, information-dense content that provides more than surface-level coverage. Adding statistics, data points, and citations increases the likelihood that your passages will be selected, but the most valuable signal comes from the unique expertise you bring into the market.

5. Personalizing for Your ICP

On-page SEO for LLMs is less about stuffing keywords and more about anticipating the questions your ideal customer profile (ICP) is likely to ask. Generative engines expand user prompts through query fan-out, pulling in adjacent or related sub-questions. Pages that cover these variations in clear, structured ways are far more likely to be captured and cited.

Future Proofing LLM SEO

The pace of change in large language models (LLMs) is unlike anything SEO has faced before. In just the past five to seven years, these systems have advanced from basic transformers like BERT—which proved machines could parse syntax and understand semantics—into today’s generative models that not only interpret but also synthesize knowledge. Modern LLMs capture fine-grained statistical, syntactic, and semantic signals, enabling them to generate coherent, context-aware answers grounded in fact-dense and well-structured sources.

For SEO, this shift means the rules are evolving faster than ever. It’s no longer enough to optimize for rankings alone; sites must now be engineered for knowledge capture, grounding, and citation inside generative engines. Future-proofing your strategy requires anticipating where LLMs are moving next, building pages that are structured for machine readability, and ensuring your expertise is consistently recognized across evolving AI-driven search ecosystems.

Step 1: Ensure All Data is Readable to LLMs

As LLM-driven search becomes the default, one of the most important technical shifts is ensuring that all of your data is machine-readable. LLMs don’t just parse your HTML—they aggregate signals across multiple sources including sitemaps, structured feeds, Google Merchant Center, Google Maps, and even third-party business listings. If these signals are fragmented, inconsistent, or blocked, your brand’s visibility inside AI-generated answers diminishes.

Step 2: Begin Hyper-Personalizing Content

Google’s patent US20240362285A1 reveals how deeply personalization is being built into the future of search. By leveraging first-party data—from demographics to life events—Google aims to make search experiences more accurate and context-aware. For SEO, this means that generic, one-size-fits-all content is no longer enough.

Step 3: Think Like Prompt Commands and Tasks

As AI systems evolve from answering questions to executing tasks, the way brands appear in search is shifting. In Google’s AI Mode, ChatGPT, and similar environments, users are beginning to issue command-style prompts (e.g., “Change phone providers for me, let’s begin the process”). This moves discovery beyond informational queries into task-oriented workflows, where LLMs guide users step by step through actions.

Supporting this shift, Josh Blyskal from Profound analyzed tens of millions of ChatGPT prompts and found that a new “Generative” intent type already accounts for 37.5% of AI prompts. Users increasingly expect AI to do the work—writing, creating, or executing tasks—rather than just delivering information. Transactional prompts (e.g., “draft my Amazon return”) also appear 9× more often in AI search than in Google search. This proves the next frontier of SEO isn’t just about visibility in answers, but positioning your brand where task execution happens inside the chat itself.

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