The category went from non-existent to crowded in about eighteen months. AI Overviews started rolling out across Google. ChatGPT and Perplexity ate noticeable share of the queries that previously went to search. Agencies coined LLMO, GEO, AEO, and several other acronyms in rapid succession. The terminology is messier than the underlying practice.
The four names you will see
- LLMO — Large Language Model Optimization. Came from the technical and developer-facing side around 2023. The acronym reads cleanly in technical writing.
- GEO — Generative Engine Optimization. Formalized by Aggarwal et al. in a 2023 Princeton paper. Picked up faster by SEO discourse than LLMO did. Currently dominates the SERP for the head term.
- AEO — Answer Engine Optimization. Older term that originally covered the rich-snippet and featured-snippet optimization era; some writers have extended it to cover AI answers.
- AI SEO — the catch-all phrasing in mainstream marketing copy. Less precise than the others; useful as a hook in non-technical writing.
All four describe the same operational practice with different emphases. Pick the term that fits your audience. The underlying mechanism is what matters.
What LLMO is not
Three things the category gets confused with.
- It is not prompt engineering. Prompt engineering is the practice of writing prompts that get useful outputs from an LLM. LLMO is the practice of writing content that LLMs retrieve and cite. Different problem, different audience, different output.
- It is not training-data optimization. Most publishers cannot directly influence what a foundation model is trained on. LLMO operates at the retrieval and citation layer, which most engines run on top of the trained model. The work targets what gets surfaced, not what gets trained.
- It is not just adding FAQ schema. FAQ schema helps with some retrieval mechanisms. It is not the practice itself; many high-citation pages have no FAQ schema, and many low-citation pages have rich schema markup.
Why the category emerged
Two structural shifts in search behavior happened in roughly the same window.
The first was the rollout of AI Overviews on Google. For many queries, the answer appears directly on the SERP, synthesized from cited sources. Click- through to the source declined; citation share inside the synthesized answer became the relevant metric.
The second was the rise of ChatGPT, Perplexity, and similar interfaces as primary search alternatives. Queries that previously went to Google now go to chat-style interfaces; the answers come from retrieved passages cited inside the model’s synthesis. The same retrieval-and-citation pattern applies.
Both shifts created demand for optimization aimed at retrieval-based systems rather than ranking-based ones. LLMO and its synonyms fill that gap.
Where the practice settled
Six structural moves dominate working LLMO across the engines that matter currently (ChatGPT, Perplexity, Google AI Overviews, Claude).
- Extractable definitions in the first 100 words of every cluster page.
- Entity-named headings throughout (no pronouns, no generic labels).
- Network-level consistency: same entity, same definition, every page.
- Comparison tables on bridge pages where queries span two clusters.
- Common-misconception blocks where the topic has training-time ambiguity.
- Visible author attribution and updated dates for E-E-A-T inheritance.
Each move targets a specific retrieval signal. See how LLMs pick sources for the mechanism behind each.