The New Architecture of SEO
How to gear up and get ready for LLM SEO
Written by
AI search
Dec 10, 2025
The fundamental mistake most SEO managers make with AI search is treating it like a crawler problem. It isn’t. It is a comprehension problem.
Traditional SEO taught us to optimize for algorithms that match keywords to links. But Large Language Models (LLMs) do not rank pages; they synthesize answers. When ChatGPT or Perplexity encounters your content, they are not indexing it—they are reading it.
This shift requires a move from "keyword optimization" to "extraction optimization." Your content’s visibility now depends on three mechanical factors: how easily an answer can be extracted, the density of your supporting evidence, and your citation authority.
Here is how to rebuild your SEO strategy for the extraction layer.
Micro-Pages Over Monoliths
For a decade, SEO best practices dictated creating "ultimate guides"—5,000-word monoliths covering every aspect of a topic. In the age of AI search, this strategy is failing.
LLMs prefer specific, self-contained answers. They treat the web as a knowledge graph, looking for precise nodes of information.
The Strategy:
Shift from broad topic clusters to focused Micro-Pages.
Old Way: A giant guide on "Marketing Automation."
New Way: A distinct page for "Marketing Automation ROI for B2B SaaS."
When an AI constructs an answer, it often pulls the definition from one source, the pricing from another, and the implementation steps from a third. By creating focused content that definitively answers one specific question, you increase the probability of being the source cited for that specific attribute.
The Schema Imperative
Schema markup has moved from "nice-to-have" to "critical infrastructure."
LLMs rely heavily on structured data to validate their "understanding" of a page. If an AI is unsure about a fact, it looks for the structured data that confirms it.
FAQ Schema: This is the most direct way to feed the "extraction layer." By wrapping core questions and answers in schema, you effectively hand-feed the model the exact snippet it needs to generate a response.
Author Authority: Anonymous content is being de-prioritized. AI models increasingly look for "expertise signals." Using distinct Article schema that links to an author’s bio and credentials helps the model verify that the source is trustworthy enough to cite.
Platform-Specific Optimization
While the underlying tech is similar, the major platforms favor different content "flavors."
Perplexity biases toward recency and citations. It heavily weights content published or updated in the last 90 days and often pulls from forum discussions (Reddit/Quora) to find "consensus."
ChatGPT biases toward instructional clarity. It favors content that sounds authoritative and instructional (e.g., standard operating procedures, documentation, and well-structured "How-To" guides).
Google AI Overviews biases toward domain strength. It is the most conservative of the three, still relying heavily on traditional domain authority signals while prioritizing direct answers that appear "above the fold."
The Prompt Gap
Traditional rank tracking is irrelevant here. You can rank #1 on Google and be invisible in ChatGPT.
The new metric is the Prompt Gap.
Instead of asking "What keywords do we rank for?", ask "What questions are users asking AI agents where we should be the answer, but aren't?"
This requires testing conversational queries (e.g., "Compare the top 3 CRMs for a startup with a limited budget") rather than just keywords ("Best CRM"). If your competitor is cited and you are not, you have a prompt gap. This is usually solved not by adding more keywords, but by restructuring your existing content to be more "extractable."
Similar Blogs
Get found in AI search
Ready to start?
