In-Model vs. Out-of-Model: How to Optimize Your Brand for AI Search and LLM Grounding
For years, SEO was about winning the blue links on a Search Engine Results Page (SERP). But the game has changed. With the rise of Large Language Models (LLMs) like GPT-4, Claude, and Gemini, users are no longer just searching—they are asking.
If your brand isn't being mentioned in AI-generated responses, you're losing a massive chunk of the modern customer journey. To win in this new era, you must understand the critical distinction between In-Model and Out-of-Model responses.
What are In-Model Responses? (The Training Data)
An "In-Model" response occurs when an AI answers a query based solely on the data it was trained on. Think of this as the AI's "internal memory."
If a user asks, "What are the best project management tools?" and the AI lists your brand without browsing the web, you have achieved In-Model visibility. This means your brand was mentioned frequently and positively enough across the web during the model's training phase to be considered a factual authority.
What are Out-of-Model Responses? (LLM Grounding)
An "Out-of-Model" response happens through a process called Grounding (often powered by Retrieval-Augmented Generation, or RAG). Instead of relying on memory, the AI performs a real-time search of the live web to find the most current information before synthesizing an answer.
When an AI says, "According to a recent article from Moz..." or provides a citation link to your website, that is an Out-of-Model response. This is the "new SEO"—the ability to be the primary source the AI chooses to retrieve during a live search.
Why This Matters for Your SEO Strategy
Understanding this divide is the difference between a stagnant strategy and a future-proof one. Here is why it matters:
- Authority vs. Recency: In-model visibility proves long-term authority, but out-of-model visibility ensures you are relevant today (e.g., pricing updates, new feature launches).
- The Citation Advantage: Out-of-model responses provide direct attribution and links, driving actual traffic to your site, whereas in-model responses may mention your brand without providing a path for the user to visit you.
- Control over Narrative: You cannot easily change what is in a model's training set (which takes months/years to update), but you can optimize your live site to influence grounded responses in real-time.
How to Optimize for AI Retrieval (The Action Plan)
To increase your chances of being the "grounding source" for an LLM, focus on these three pillars:
1. Structured Data & Schema
AI models love organized data. Use JSON-LD schema to explicitly tell the AI what your product is, who your authors are, and what problems you solve. This reduces the "effort" for the AI to parse your page.
2. The "Answer Engine" Format
Shift some of your content toward a Q&A format. By explicitly stating a question in an H2 and providing a concise, factual answer immediately below it, you make your content highly "retrievable" for RAG systems.
3. Digital PR and Brand Mentions
Because LLMs look for consensus, being mentioned on high-authority third-party sites (industry blogs, news outlets, forums) increases your probability of being both an in-model authority and a preferred out-of-model source.