How to Get Content And Brand Recommended by AI & LLMs

How to Get Content (& Brand) Recommended by AI & LLMs

Prioritizing visibility in the generative search era.

Why AI & LLM Recommendations Matter

AI and large language models (LLMs) such as ChatGPT, Google Gemini, and Claude become more influential in how people discover information, and brands must learn how to optimise their content for AI recommendations—not just search engines.

Making your content AI-friendly, LLM-relevant, and deeply aligned with Google’s E-E-A-T principles: Experience, Expertise, Authoritativeness, and Trustworthiness.

From Google’s SGE (Search Generative Experience) to OpenAI’s ChatGPT Browse, AI-powered systems are now directly answering queries, summarising sources, and recommending brands.

What makes LLMs recommend certain content?

Large amounts of online data are used to train AI systems. When generating responses, they rely on patterns that signal high-quality, trustworthy, and expert content. If your brand doesn’t reflect those signals, you’re less likely to be cited or surfaced.

 LLM Seeding

LLM seeding represents a fundamental shift in how brands are achieving online visibility and authority. As AI systems increasingly mediate information discovery, traditional approaches focused solely on search engine rankings miss critical opportunities for brand exposure through AI citations.

The strategy provides a comprehensive framework for getting your brand consistently mentioned by AI models. From understanding how AI systems source their training data to implementing technical optimizations that maximize citation likelihood, each element contributes to building lasting AI visibility.

Improving Brand’s Visibility in AI-Based Search using LLM SEO

With the rise of generative AI and intelligent assistants like ChatGPT, Gemini, and Claude, brands are facing a new frontier: AI-based search powered by Large Language Models (LLMs).

These models are redefining how information is discovered, prioritised, and presented. Consumers are no longer clicking through links; they are getting instant, summarised responses directly from AI. And unless your brand is optimised to be part of that response, you’re invisible.

LLM SEO, or Large Language Model Optimisation, is an emerging discipline that focuses on how brands can shape their content, presence, and credibility in ways that align with how LLMs process and recall information.

As large language models take centre stage in shaping how information is discovered online, LLM optimisation SEO is no longer a choice; it’s a necessity.

LLMs learn about your brand by -

1. Crawling public content
2. Capturing context and tone
3. Identifying entities and relationships
4. Tracking brand mentions across the web
5. Analysing customer interactions
6. Updating from new data sources

AI Inputs, not AI Outputs

Some of the discussions that are creating data-driven or research-based content work for getting AI recommendations. Given the dearth of true data-driven content that AI craves, enjoy it while it lasts, as that would only work in the short term.

If you are using LLMs in “deep” mode on a premium subscription to inject more substance and value into your content, that simply won’t make the AI-quality cut.

AI favors answer-first sites such as Reddit and Wikipedia. Structuring and optimizing branded content that LLMs actually cite in their responses.

AI is changing the way searching happens- and that, in turn, has changed how discovery works. With tools such as Perplexity, ChatGPT, and Gemini, the way they crawl the web is completely different from how search engines once did.

Instead of finding web pages as a response to a user’s query, they gather and serve answers. And that has great appeal. The content that ends up cited inside AI-generated responses isn’t always the ones that rank highest on Google search engine result pages [SERPs]. Instead, AI search models lean toward content that’s easy to retrieve, clearly written, and straightforward for them to interpret and quote.

SEO Basics Still Matter

The reverse engineering of search engine results pages to direct content strategy and formulation was effective because rank position is a regression problem.

However, there are some heavy overlaps that won’t go away and are even more critical than ever. Unlike SEO, where more word count was generally more, AI faces the additional constraints of rising energy costs and shortages of computer chips. It means that content needs to be even more efficient than it is for search engines for AI to break down and parse meaning before it can determine its value.

So, by all means:

• Code pages for faster loading and quicker processing.
• Deploy schema for adding context to the content.
• Build a conversational answer-first content architecture.
• Use HTML anchor jump links to different sections of your content.
• Open your content to LLM crawling and use llms.txt file.
• Provide programmatic content access, RSS feeds, or other.

Human, Not AI-Written

If the quality of the content (let’s assume it even includes information gain) is on point, then AI shouldn’t care whether it was written by AI or a human. While it’s unlikely that generative outputs are tagged in any way, it’s pretty obvious to humans when content is AI-written, and it’s also pretty obvious statistically to AI engines, too.