In and Out of Model Responses Explained — Whiteboard Friday

03/06/26
Source: moz (en-US). Read the original article

TL;DR Summary of Preparing Your SEO Strategy for LLM Grounding and AI Search Visibility

The key to optimizing SEO for large language model (LLM) grounding lies in understanding the difference between in-model and out-of-model responses. In-model responses rely solely on the model’s training data, which updates infrequently, making quick influence difficult. Out-of-model responses use live web retrieval, enabling faster impact through strategies like barnacle SEO, digital PR, and updating your own site. By leveraging these approaches, brands can better dominate visibility in AI-powered search results.

Optimixed’s Overview: Mastering SEO in the Era of AI-Powered Search and LLM Grounding

Understanding In-Model vs. Out-of-Model Responses

When engaging with AI-driven search tools and LLMs such as GPT-4, responses can originate from two main sources:

  • In-model responses: Generated purely from the LLM’s pre-existing training data, which is static and refreshed only periodically (e.g., GPT-4.0 trained up to late 2022, GPT-4.5 up to mid-2024). These responses cannot be quickly influenced by new content.
  • Out-of-model responses: Created by the LLM when it performs external, live web searches to retrieve current or factual information. This hybrid approach is increasingly used in Google’s AI features and similar tools.

Why Out-of-Model Responses Present SEO Opportunities

Out-of-model responses incorporate fresh data from the web, meaning SEO efforts can have a faster effect on AI-generated answers. This opens the door for brands to influence search visibility by:

  • Barnacle SEO: Expanding your presence beyond your primary website by optimizing other authoritative platforms you control or contribute to—such as LinkedIn, Wikipedia, YouTube, Medium, and social profiles—to capture non-branded and topical queries.
  • Digital PR: Building relationships and securing placements on high-authority third-party sites to shape the external content that AI systems use for grounding their responses.
  • Updating Your Own Site: Ensuring your website content is timely, relevant, and optimized for queries that your brand targets, so it remains competitive in live web retrieval results.

Strategic Implications for SEO Professionals

SEO practitioners must rethink their approach by distinguishing which queries are likely to be answered via in-model data versus those requiring current web information. This understanding guides prioritization in keyword tracking and content strategies, focusing on queries where rapid influence is possible through out-of-model grounding.

Ultimately, brands that master these techniques will not only improve rankings but also dominate AI-driven search visibility, ensuring their content is the go-to resource in evolving AI search experiences.