Does AI Optimization Differ for Each LLM?

There’s a lot of buzz around whether AI optimization differ for each LLM. So, we decided to address the question.

Now we know that each LLM is trained on a dataset gathered by its manufacturer. But is it safe to assume all of them use the same standards when picking sources to cite?

Not necessarily. Now, what does this mean for webmasters trying to optimize their website presence for AI search engines? Do they need to optimize for each platform separately, or can an identical strategy do them justice? Let’s find out.

Do LLMs Differ From Each Other?

Not all LLMs are created equal. Each model is shaped by its developers’ design choices. From the architecture and training data to ongoing fine-tuning and updates, they’re systematically different but theoretically the same. 

First, let’s break them down by similarities and differences to get a more detailed picture. 

Identical Features Between LLMs 

• All LLMs are pre-trained on massive datasets, including internet text, books, websites, articles, and code.
• They are prone to hallucinations, which may lead to the synthesis and distribution of inaccurate information. 
• Their citations range from articles to forum discussions, social media, landing pages, e-books, and research journals.
• Most LLMs have capability limitations and may produce different answers in paid and free versions. 
• Each model breaks up queries into tokens, or individual units of text, to better understand the context of a sentence. 

What Makes LLMs Different From Each Other?

• The extent of pre-training and fine-tuning of a model determines how well it handles prompts, including everyday questions to complex tasks like advanced mathematics or code generation.
• They generate responses in varying time windows. Gemini is the fastest.
• Their architecture, training philosophy, and API designs vary.

Popular LLMs & Their Optimization

Let’s look at each model separately to analyze its individual generative traits and determine how to approach optimization.

This will also help analyze if AI SEO differs by LLM type or if a combined optimization strategy could be implemented to boost visibility across all platforms. 

ChatGPT

ChatGPT is pretrained on vast datasets of information. However, it does not rely on real-time retrieval, indicating it doesn’t frequently implement RAG to generate answers. 

OpenAI explains the foundation model on which its products, including ChatGPT, are trained. They claim the information is primarily extracted from:

1. Websites and sources available on the internet
2. Information obtained via third-party partners
3. User data, human trainers, and researchers

An analysis by Kevin Indig in the Search Engine Journal revealed that 44.2% of ChatGPT citations come from the top 30% of content on a page. Moreover, 56% of its journalism citations come from articles published within the last year. This highlights that ChatGPT prioritizes recency and clarity in the top section of content, clearly indicating where optimization efforts should be centered. 

If you want to stay visible, it’s important to understand how AI crawls the web and which type of content is most likely to get picked up. For ChatGPT, relying on our three-principle content creation manifesto can help boost visibility. 

Introduction: Should be short and crisp and answer the question up front.

Mid-Section: Mention key features and important insights; go into the details, but avoid dragging on in this section. 

Conclusion: Finish off with a comprehensive summary of the topic and article. Keep it clear and concise.  

Claude

Claude has many models with slight variations in training, but most of them build on synthetic data from its previous models. The latest model, Opus 4.7, is trained on a combination of information sources, including online sources and public/private datasets. 

Besides this, Claude also utilizes the RAG technology to pull up answers after a conversation’s context window runs low. The LLM usually processes 200K tokens, but if the limit is exceeded, it switches over to RAG mode to expand its response capacity. 

Muck Rack’s research about AI expanded on how Claude picks sources to cite. Here are some key findings from their study. 

• Higher citation frequency for mid-tier media outlets rather than major publications like the NY Times, The Times, or Forbes. 
• It cites platforms like NPR, Yahoo Finance, Variety, and CNN more often than ChatGPT or Gemini. 
• It has a 3x higher chance to cite sources from 2-4 weeks ago compared to ChatGPT. 

In simple words, Claude prioritizes sustained relevance and niche authority over recency and trends. 

Gemini

Gemini is part of the traditional search ecosystem, weaving into the existing framework of search with an interconnected network that draws from your familiar G Suite resources. Here’s Google’s own disclaimer about Gemini training.

“We trained Gemini 1.0 at scale on our AI-optimized infrastructure using Google’s in-house designed Tensor Processing Units (TPUs).”

It’s also a multimodal tool, meaning it works on different sections of search, including AI Overviews, AI Mode, and the standard Gemini search bar. This means that if you’re optimizing content for the LLM Gemini, you’re essentially boosting your presence across all these Google search capabilities. 

Unlike Claude and ChatGPT, Gemini natively handles video, audio, and images. It’s also frequently seen pulling up images, local search packs, and other Google features in response to queries, highlighting that it’s closely knitted to Google’s generic guidelines for SEO visibility. 

Which brings us to this conclusion: if you’re visible on the SERPs, you may have a higher chance of getting cited in Gemini too. 

How To Prioritize?

So now that we know how each model operates, it’s critical to note that AI search optimization techniques for LLMs can also differ. If you’re targeting a specific generative tool, you can fine-tune your strategy to exactly what the LLM prioritizes. 

But it’s also important to note that these are minor differences, which means that it’s not necessary to have an optimization plan exclusive to each LLM in case you wish to boost AI visibility across all generative tools. 

Here’s a combined strategy that works for brands with limited budget or resources. 

1. Structure content for AI

Use clear headings, logical flow, and direct answers. Generative AI tends to favor content that is well-organized and easy to parse. 

2. Make sure AI can crawl your content

Check that your pages aren’t blocked by robots.txt rules that inadvertently exclude AI crawlers. Consider adding an llms.txt file to your site.

3. Answer questions directly

A primary rule when optimizing content for LLMs is to directly answer the how, what, where, and why questions. 

4. Build topical authority, not just individual pages

AI systems favor sources that demonstrate deep, consistent expertise on a subject. 

5. Consistently update existing content

Regular audits to correct facts, refresh statistics, and remove stale information help maintain visibility across all generative tools.

6. Earn backlinks and mentions from credible sources

Much like traditional SEO, being referenced by reputable websites and publications signals trustworthiness to AI systems. The more your content is treated as a source by others, the more likely AI is to treat it as one too.

Prioritize:

• Editorial mentions in niche publications
• Citations from pages that already rank as definitional references
• Original data campaigns (so others must cite you)

And with that, we answer the much pressing question of “Does AI optimization differ for each LLM?” For more on AI optimization and citation engineering, stay tuned.