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SEO for ChatGPT (GEO) · Guide 4 of 22

LLM SEO: SEO for language models, explained honestly

LLM SEO is the technical term for the same thing as GEO or AEO: optimizing for Large Language Models. What matters isn't the acronym, it's understanding why an LLM cites certain brands and not others — and how you legitimately influence that decision, without tricks that stop working in six months.

What an LLM is (the minimum you need to know)

A Large Language Model (LLM) is a system trained over huge amounts of text that predicts the most likely next word in a sequence. That simplification produces surprising outputs: apparent reasoning, coherent writing and, for what concerns us here, the ability to summarize and cite sources when answering a question. To understand LLM SEO you don't need more theory than this: the model answers based on what it learned during training and, when it can, completes that with real-time searches.

How an LLM chooses what to cite

Three independent signals, weighted depending on the prompt type. Knowing them tells you exactly where to invest effort and where not to.

Signal 1: presence in training data

Whatever your brand has produced or been mentioned in across public sites, books, forums and papers over the past few years is part of the model's knowledge. This signal is retroactive — you can't change what's already there — but you can feed the future corpus with substantive content that gets widely cited.

Signal 2: real-time retrieval (RAG, browsing)

For questions that need fresh information, the model searches the web in the moment. Here you compete prompt by prompt: your indexable site, with recent, well-structured content, enters the race against the rest.

Signal 3: citable content structure

When the model retrieves your page, it decides which fragments to extract. A self-contained paragraph, a clean table or a structured list gets extracted with very high fidelity. A long paragraph full of asides gets ignored or summarized poorly. The shape of the content decides whether you get quoted literally or left out.

LLM SEO strategy 2026 (which signal gets which priority)

Not every signal applies equally depending on stage and brand type. This is the reasonable prioritization for 2026:

Your situationPriority 1Priority 2Priority 3
New brand or unclear entityStructure (schema + format)External authorityCorpus presence
Established brand with solid SEOStructure (reformatting)Corpus presence (serious content marketing)External authority
Enterprise brand with historyCorpus presence (PR + studies)Structure (advanced schema)External authority
Regulated market / very technical B2BExternal authority (papers, regulation)StructureCorpus presence

Benchmark: how to measure your LLM visibility

Without measurement, the above is theory. The minimum benchmark for it to be serious:

  • A battery of 50-200 prompts representative of your category, fixed over time.
  • Run against 3-4 engines (ChatGPT, Perplexity, Google AI, optionally Claude).
  • Weekly minimum frequency.
  • Metrics: mention rate, relative position, sentiment, share of voice, weekly trend.
  • Comparison against 3-5 direct competitors.

Frequently asked questions

In practice, yes. Each acronym was coined by someone else to sell the same service. GEO (Generative Engine Optimization) is the most used, AEO (Answer Engine Optimization) focuses on direct answers, LLM SEO is the most technical. If you meet someone defending the difference passionately, they're probably selling you one of the three.

With a fixed set of prompts representative of your category, monitored at a constant frequency (weekly minimum), measuring mention rate plus sentiment plus relative position vs. competitors. If the tool you use doesn't measure at least that, it measures air.

Several from day one. 70% of the work is common to all (entity, schema, citable content). The remaining 30% is fine-tuning per engine. Focusing only on ChatGPT repeats the early-SEO mistake when everyone optimized only for Google ignoring Bing and paid the toll later.

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LLM SEO: SEO for language models, explained honestly · Implementa