China AI visibility · Definitional reference
What is LLM optimization?
LLM optimization (LLMO) is the youngest of the three big AI-search acronyms. It emerged in vendor marketing in 2025 with no canonical academic paper. The legitimate distinct meaning is model-layer optimization — measuring training-data presence, running cross-vendor prompt panels, analysing token-level citation patterns. Most other usage is GEO with a different label. Here is the working definition, the small but real distinction from GEO, and when the LLMO frame is actually the right one.
Last reviewed 2026-05-10. Citations to peer-reviewed papers and industry vendor sources throughout.
The one-sentence definition
LLMO is the practice of optimising for the underlying language model — its training data, its cross-vendor recall behaviour, its token-level citation patterns — rather than for any single AI product surface (ChatGPT search, Perplexity answers, Gemini summaries).
Where the term came from
LLMO has no canonical origin paper. Unlike GEO (which has Aggarwal et al., KDD 2024) or AEO (which has the 2019–2022 Featured-Snippet lineage), LLMO emerged in vendor marketing through 2025 as several SaaS firms looked for differentiated language. The term shows up across measurement-tool blog posts, agency landing pages, and industry-press explainers — but no peer-reviewed work uses LLMO as its canonical term. The closest authoritative treatment is Neil Patel's 2025 explainer, which treats LLMO, GEO and AEO as a "rolling rebrand of the same field."
Profound — currently the most-funded specialist in this space at a $96M Series C raise per their announcement — has gone further. Their AEO-vs-GEO post argues "AEO and GEO are the same thing", and by extension treats LLMO as redundant. We think there is a legitimate distinct meaning when the work is genuinely model-layer (described below) but agree that 80%+ of LLMO marketing usage is repackaged GEO.
Net read: LLMO is real but narrow. If a tool says "we do LLMO" and what they actually do is page-level optimisation for ChatGPT and Perplexity, they are doing GEO and using a fresher label. If a tool measures cross-model recall, training-data presence, or token-level citation behaviour, the LLMO label fits.
What LLMO actually means in practice
The legitimate distinct work clusters around four activities that GEO does not centrally emphasise:
1. Cross-model prompt panel testing
Running the same prompt against GPT-5, Claude 4, Gemini 2, Llama 4 and DeepSeek V3 — does brand recall persist? Where does each model surface different sources? This is the work that exposes whether a brand has actual training-data presence versus retrieval-time visibility through a particular product's web-fetch layer. Aggarwal et al.'s KDD 2024 benchmark used cross-model prompt panels (n=10,000 queries, multiple engines) — the methodology is established, the LLMO framing is what's new.
2. Training-data presence audits
The "do the models know about us without web fetch?" question. Methods: ask the model the same prompt with web search disabled, compare responses across model versions, look for model-specific phrasing patterns that suggest training-corpus origin. This is genuinely model-layer work — a brand can be invisible in retrieval today but cited verbatim from training data, and vice versa.
3. Token-level and citation-pattern analysis
Tw93's 2026 instrumentation of ChatGPT showed the engine retrieves roughly 100 pages per query but only ~15% surface in the answer. Token-level work asks: which 15%, and what shape gets extracted? Brands optimising at this layer do A/B tests on phrasing, structure, citation density to see what the model preferentially extracts as quotable chunks. This goes beyond GEO's "evidence density per 100 words" rule into genuine model-behaviour science.
4. Vendor-agnostic measurement
LLMO measurement reports brand presence at the model family level (GPT, Claude, Gemini, Llama, DeepSeek) rather than the product level (ChatGPT, Claude.ai, Perplexity). The argument: a brand's presence on the GPT family is more durable than its presence on any one ChatGPT UI, because OpenAI iterates the chat product faster than they iterate the foundation model.
How LLMO actually differs from GEO
| Dimension | GEO | LLMO (when used legitimately) |
|---|---|---|
| Optimisation target | The product surface (ChatGPT answer, Perplexity citation) | The model layer (GPT-5, Claude 4, etc.) |
| Measurement method | Citation selection vs absorption vs mention (Aggarwal KDD 2024 + Yao Jingang 2026) | Cross-model prompt panel + training-data audit |
| Tactical emphasis | Page-level evidence density + third-party source-graph (third-party cites ~6.5× more effective than self-citation per Aggarwal) | Cross-model recall testing + token-level citation pattern analysis |
| Time horizon | Days (technical) → weeks (content) → quarters (off-site) | Months — training-data presence builds slowly across model retraining cycles |
| Volume of canonical research | Aggarwal KDD 2024, Yao Jingang 2026, SE Ranking 2025, Williams-Cook 2026 — substantial | Sparse; mostly vendor whitepapers |
When the LLMO label is the right one
Three scenarios where calling the work LLMO genuinely clarifies:
- Multi-vendor audit work. When you are explicitly testing brand recall across model families to compare. "We're doing LLMO" is more accurate than "we're doing GEO" if the deliverable is a cross-model comparison report.
- Training-data presence work. When you are building toward training-data inclusion (via Wikipedia entries, ArXiv preprints, GitHub README projects, large public datasets) and explicitly distinguishing that from retrieval-time visibility.
- Token-level testing. When you are running controlled experiments on phrasing/structure/citation-density variants to learn what shape the model preferentially extracts.
When the work is "we want our brand in ChatGPT search-mode answers" — that is GEO, not LLMO. When the work is "we want a Featured Snippet" — that is AEO. Use the most specific term that fits.
Why China is a separate model-layer problem entirely
One implication of the LLMO frame: training data matters. The Chinese AI engines (DeepSeek, Qwen, Doubao) train on different corpora than the Western models. A brand with rich Wikipedia + Reddit + ArXiv presence has model-layer training-data visibility on GPT-5 and Claude 4 — but that same training-data presence does not transfer to DeepSeek V3 or Qwen 3, which weight Mainland Chinese sources (百度百科, 知乎, 微信公众号, 36氪, 虎嗅, Xiaohongshu, SMZDM) far more heavily.
In our 540-call panel (May 2026), top-15 cited-source overlap (Jaccard) between any two Chinese engines was 0.20–0.30. Overlap between Western and Chinese engines is lower still. The implication: training-data work for Western LLMO does not transfer to Chinese LLMO. They are separate optimisation problems with separate substrates. For the China-specific treatment see China AI visibility, DeepSeek SEO, Qwen optimization, and Doubao optimization.
See what your brand looks like inside an AI answer
Whether you call it GEO, AEO or LLMO, the diagnostic is the same: run your URL against a stratified prompt panel and read the output. Eastbound's free audit covers DeepSeek, Qwen and Doubao on a Mainland-CN consumer prompt panel and reports per-engine selection, absorption and brand-mention scores. No login.