Why you can’t rank on ChatGPT and other LLMs

Last updated on October 17th, 2025 at 01:58 pm

Probably the most important misunderstanding in the search marketing industry today is not whether we should respond to the rise of generative AI. Response is inevitable.

The real problem is that most teams are treating this shift as just another algorithm update, assuming these models behave like search engines. They don’t.

This reaction is rooted in discomfort; faced with a black box most don’t really understand, teams default to familiar tactics and clients stress agencies with impossible expectations.

Impossible in the most technical term, like “ranking” on LLMs.

Agencies reach for what they already know, rebrand it as AEO, and hope that the old playbook still brings the same measurable results. But it doesn’t. If we want to influence how LLMs engage with our content, we have to start learning how the system actually works, thus accepting that it can only be partially and probabilistically influenced; not controlled.

Impossible in the most technical term, like “ranking on ChatGPT” or generally on LLMs.

Instead of ‘ranking,’ you’ll need to optimize your pages for the fragments models actually use (see how LLMs extract and quote snippets.)

Agencies reach for what they already know, rebrand it as AEO, close their eyes and hope that the old playbook still brings the same measurable results. But it doesn’t.

If we want to influence how LLMs engage with our content, we have to start learning how the system actually works, thus accepting that it can only be partially and probabilistically influenced; not controlled.

You can watch my podcast episode with Dinghy Studio, or keep reading for the full breakdown.

Reason 1: LLMs don’t use a Ranking Algorithm like Search Engines do

Large Language Models like GPT-5 are trained using a process called pre-training, where the model processes hundreds of billions of tokens from massive datasets, including Common Crawl, Wikipedia, books, academic papers, and code repositories.

The model learns statistical patterns, not exact facts, and encodes them into billions of parameters (weights) inside its neural network.

I’ll explain exactly what this means in Chapter IV.

Once pre-training ends, the model’s weights are frozen. This means:

While the core weights of models like GPT-5 remain frozen after pre-training (meaning you cannot inject new knowledge into their neural network without retraining), some new architectures use modular systems that allow parts of the model to be updated or extended via:

• Retrieval-Augmented Generation (RAG): dynamically fetching current information from search APIs or vector databases.
• Memory Modules (e.g., ChatGPT’s “custom GPTs” or persistent chat memory): storing user-specific preferences or facts outside the model weights.
• Tool Use and Plug-ins: calling APIs or calculators to fetch real-time information, not generated from internal knowledge.

This could lead to think something has changed, however, none of these mechanisms alter the model’s internal knowledge base (the billions of statistical weights learned during training).

✓ The model can “look up” new facts via tools or retrieval.

✖ The model cannot “learn” new facts in the traditional way unless it is retrained or fine-tuned.

Reason 2: Training Data isn’t a live index of the web

Unlike search engines, LLMs, even as of August 2025 with the release of GPT-5 and Gemini 2.5 Pro:

• Don’t systematically use a crawler that updates constantly.
• Don’t respond to sitemap.xml or Search Consoles submissions.
• Don’t use page freshness as a visibility signal.
• Don’t store full documents, but statistical abstractions.

This means there is no equivalent to SEO submission protocols like:

• Indexing APIs
• URL inspection tools
• Backlink tracking
• … and more

However, it is important to note that as of August 2025 OpenAI’s GPTBot and ClaudeBot by Anthropic do crawl the web on an ongoing basis.

While these crawls are not tied to live model updates, they might feed future snapshots of the model, or more likely support faster retrieval-augmented systems like ChatGPT-5 and similar releases.

Reason 3: LLMs are not Search Engines. They are Generators.

As introduced, it’s tempting to think that platforms like ChatGPT, Claude, or Google SGE / AIO / AIM work like search engines. But they don’t.

They are Large Language Models (LLMs) → statistical generators, not retrieval engines.

LLMs do not evaluate, rank, or index documents like search engines do. They generate responses based on either:

• Pre-trained internal knowledge, or
• On-the-fly retrieval of external text, used to guide or ground the generation.

This second approach, known as retrieval-augmented generation (RAG), is what powers systems like:

Platform	Core Model	Retrieval Behavior	Citation Behavior
ChatGPT (GPT‑5)	Deep Research (autonomous web reports) & Agent Mode (multi-step tool execution)	Links shown in research reports and agent interactions	Cites sources in Deep Research outcomes; Agent Mode shows process steps and reference context
Microsoft (Copilot Suite)	GPT-5 integrated with real-time router across M365, GitHub, VS Code	Includes links in structured outputs	Citations are clear when referencing external content; integrated across apps
Google Gemini (1.5 Pro / 2.x)	Google search index + ecosystem tools	Selective linking	Snippet-based answers; links more prominent when using tools
Perplexity.ai	Live crawling + custom index	Frequently includes URLs	Strong inline citations with multiple sources visible
Claude (Anthropic)	Optionally live web search for paid users	Links when context provided	Generates citations only under explicit source context
xAI Grok 4	Real-time web and X/Twitter search	Contextual storytelling	Informal, narrative citation style—less formal sourcing

So if you don’t rank on chatGPT, what’s actually happening?

Most of these systems don’t “rank” documents the way Google Search does. Instead, they:

1. Rewrite the user’s query into internal sub-questions or semantic fragments
2. Retrieve supporting content, often in the form of titles, snippets, and intros
3. Compose an answer, selecting and synthesizing fragments based on internal scoring: fluency, clarity, and helpfulness; not traditional authority.

Even when citations are shown, they are:

• Not guaranteed to have a link
• Not complete or reliable
• Not always attributed

In other words: your site might be quoted, paraphrased, or ignored and you won’t know unless you test the prompt yourself.

And the core issue remains: LLMs don’t understand what they’re saying

Whether pre-trained or grounded in live search, LLMs do not reason or verify. They don’t actually “understand” or “know” facts. LLMs complete patterns.

Their answers are fluent approximations, not factual commitments, and that’s true across:

• ChatGPT (OpenAI)
• Claude (Anthropic)
• Gemini (Google)
• Perplexity (independent)
• Bing Copilot (Microsoft)

Some simulate reasoning better than others. Actually, from experience, I would say depending on your luck with one specific chat session, one might simulate reasoning better, and another could collapse, resulting in an infuriating behavior. But none of them retrieve or cite in the way marketers are used to from traditional search engines.

Reason 4: your web or app property isn’t “In ChatGPT”unless it was in the Training Set or Search Results

As we are introducing the concept that citations are not guaranteed or reliable, one would want to be “present” in the internal knowledge of the model. A critical realization for any digital team: you are invisible to LLMs by default.

Differently, your content could be used on the fly and for a single user experience, if:

• It is discoverable through a live search engine API (Bing Search, Google Programmable Search, Perplexity’s crawler, Claude’s web lookup)
• It is structured in a way that is machine-readable and extractable in real time
• You are a commonly cited source in public datasets (e.g., Stack Overflow, Reddit, academic databases)

Even in browsing mode, visibility depends on how search engines index and display your content, not on LLM awareness.

With this, I am starting to introduce the point for SEO and Organic Search Engineering. SEO is not dead, as usual. It’s even more important than we could fathom years ago, and it’s going to stay crucial for every search API call in the future.

In Summary:

• You cannot force inclusion into ChatGPT’s knowledge.
• You cannot update the model manually.
• You cannot push fresh content (unless it’s linked or included in a source ChatGPT can retrieve in browsing mode).
• You cannot learn what the model “knows”; only test what it says
• Optimization must shift from “ranking” to “being found , understood and chosen.”

It’s necessary to stop thinking in terms of “tricking the model” and start thinking in terms of clarity, availability, and machine readability, all of which we’ll explore in the next chapters.

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