# What is LLM Optimization (LLMO)?

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.

## 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).

> **The defining feature.** GEO targets the product surface (the answer the user reads). AEO targets direct-answer extraction. LLMO targets the model layer that sits underneath all of these. The argument for LLMO as a separate discipline: chat products iterate fast and rebrand; the underlying model families (GPT-5, Claude 4, Gemini 2, Llama 4, DeepSeek V3) are more stable optimisation targets.

## 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. The closest authoritative treatment is [Neil Patel's 2025 explainer](https://neilpatel.com/blog/aeo-vs-geo-vs-llmo/), 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](https://www.tryprofound.com/blog/profound-raises-96m-series-c) — has gone further. Their [AEO-vs-GEO post](https://www.tryprofound.com/blog/aeo-vs-geo) argues "AEO and GEO are the same thing", and by extension treats LLMO as redundant. Our reading: there is a legitimate distinct meaning when the work is genuinely model-layer (described below) but ~80% of LLMO marketing usage is repackaged GEO.

## What LLMO actually means in practice

The legitimate distinct work clusters around four activities 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? Aggarwal et al.'s [KDD 2024 benchmark](https://arxiv.org/abs/2311.09735) used cross-model prompt panels (n=10,000 queries, multiple engines) — methodology is established, the LLMO framing is what's new.

**2. Training-data presence audits.** Methods: ask the model the same prompt with web search disabled, compare across model versions, look for model-specific phrasing patterns.

**3. Token-level and citation-pattern analysis.** Tw93's 2026 instrumentation of ChatGPT showed the engine retrieves ~100 pages per query but only ~15% surface in the answer. Token-level work asks: which 15%, and what shape gets extracted?

**4. Vendor-agnostic measurement.** LLMO measurement reports brand presence at model family level (GPT, Claude, Gemini, Llama, DeepSeek) rather than product level (ChatGPT, Claude.ai, Perplexity).

## How LLMO actually differs from GEO

| Dimension | GEO | LLMO (when used legitimately) |
|---|---|---|
| Optimisation target | Product surface (ChatGPT answer) | Model layer (GPT-5, Claude 4) |
| Measurement method | Selection vs absorption vs mention | Cross-model prompt panel + training-data audit |
| Tactical emphasis | Page-level evidence + third-party source-graph | Cross-model recall + token-level pattern analysis |
| Time horizon | Days → weeks → quarters | Months — training-data presence builds across retraining cycles |
| Canonical research volume | 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

1. **Multi-vendor audit work** — explicitly testing brand recall across model families.
2. **Training-data presence work** — building toward training-data inclusion via Wikipedia, ArXiv, GitHub.
3. **Token-level testing** — controlled experiments on phrasing/structure/citation-density variants.

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

Training data matters. DeepSeek, Qwen, Doubao train on different corpora than Western models. A brand with rich Wikipedia + Reddit + ArXiv presence has training-data visibility on GPT-5 and Claude 4 — but that 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. Western/Chinese overlap is lower still. Implication: training-data work for Western LLMO does not transfer to Chinese LLMO. Separate optimisation problems with separate substrates. See [China AI visibility](https://www.eastbound.ai/china-ai-visibility/), [DeepSeek SEO](https://www.eastbound.ai/deepseek-seo/), [Qwen optimization](https://www.eastbound.ai/qwen-optimization/), [Doubao optimization](https://www.eastbound.ai/doubao-optimization/).

## Related reading

- [GEO vs AEO vs LLMO — definitions and differences](https://www.eastbound.ai/geo-vs-aeo-vs-llmo/)
- [What is generative engine optimization?](https://www.eastbound.ai/what-is-generative-engine-optimization/)
- [What is answer engine optimization?](https://www.eastbound.ai/what-is-aeo/)
- [Eastbound's measurement methodology](https://www.eastbound.ai/methodology/)
- [China AI visibility for global brands](https://www.eastbound.ai/china-ai-visibility/)

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