LLMO: What It Is and How to
Optimise for Large Language Models

Why LLMO is emerging as a distinct term

For most of 2023 and 2024, practitioners used GEO (Generative Engine Optimisation) as the umbrella term for any optimisation targeting AI-generated answers. More recently, "LLMO" has appeared to describe the subset of that work that focuses specifically on the language model inference step — how a model reads, parses, and decides to cite a page — rather than the full AI search pipeline including crawling, indexing, and retrieval.

The distinction matters practically:

• GEO concerns the entire pipeline: indexing by AI crawlers, retrieval by the search layer, synthesis by the model. It includes technical factors like robots.txt configuration, crawl speed, and Bing/Google index coverage.
• AEO (Answer Engine Optimisation) focuses on structuring content to answer specific questions directly — most commonly through answer capsules and FAQPage Schema.
• LLMO narrows to the model inference step: given that a page has been retrieved, what makes the model decide to extract from it, attribute a claim to it, and cite it by name?

In practice, LLMO tactics produce content that is simultaneously better for GEO, AEO, and traditional SEO. The term is younger than the others and its usage is still settling — but it points at something real that GEO and AEO do not name precisely.

For the foundational framing of AI search as a discipline distinct from traditional SEO, see our article on AI SEO vs traditional SEO.

What LLMO optimises for: the three extraction signals

When a language model has a retrieved page in its context window, it evaluates the page for three things before deciding how to use it:

1. Answer extractability — Can the model pull a direct, self-contained answer from the page without rewriting or inferring? Pages that answer a question in the first 2–3 sentences of a section are far easier for models to extract than pages that require reading five paragraphs to reconstruct the answer. LLMO prioritises this over prose quality: a clear, slightly plain answer that can be extracted verbatim is more valuable than an elegant essay that requires synthesis.

2. Attribution confidence — Does the model have enough entity signal to attribute this content to your business by name? If your Organization Schema is missing, your business name appears inconsistently across the page, or there are no external references to cross-check, the model may cite the information without naming you — or not cite at all. LLMO builds the entity signals that make attribution confident.

3. Factual grounding — Is the content internally consistent and verifiable-looking? Models give more citation weight to pages that cite specific claims, use consistent terminology, and do not contradict themselves within the same article. This is not about writing for sceptics — it is about giving the model's consistency-checking process nothing to flag.

LLMO tactics: the concrete checklist

Structure content for extraction at every level

Every page targeting an LLMO outcome should have three structural layers:

• A page-level capsule (2–4 sentences answering the primary intent, near the top)
• Section-level mini-capsules (1–2 direct sentences under each H2 before expanding)
• FAQ block (4–6 Q&A pairs, each answer self-contained at 40–80 words)

This gives language models three extraction points: the full-page summary, the section-level summary, and the FAQ-level point-answers. A page with all three layers is far more LLMO-complete than a page with excellent prose but no structured answer anchors.

Build topical authority through entity consistency

LLMO rewards domains that cover a topic with depth and consistency. A cluster of interlinked pages using the same terminology, each with its own answer structure and internal links, signals topical authority more effectively than a single long-form article.

Terminology consistency matters specifically for LLMs: if you call your service "AI web design" on some pages and "machine learning website development" on others, the model sees two entity signals instead of one unified brand concept. Decide on your core terms and use them consistently.

Implement the Schema stack

The Schema markup stack for LLMO is the same as for GEO and AEO:

• Organization with name, url, logo, sameAs, areaServed, serviceType
• FAQPage on all pages with questions
• Article on blog posts with datePublished, dateModified, author
• Service on service pages with provider and serviceType

The sameAs property in Organization Schema is particularly important for LLMO. It points the language model to an external, independently verifiable reference for your entity — typically a LinkedIn company page or a business directory listing. This cross-reference significantly increases the model's confidence when attributing content to your brand specifically.

Write for extractability, not for search crawlers

LLMO content follows a "conclusion first" structure: state the direct answer, then explain why it is true, then provide supporting detail. Concretely:

• Open H2 sections with the direct answer, not a scene-setter
• Avoid opening sentences like "In today's fast-paced digital landscape..." (zero extraction value)
• Use specific, concrete claims ("pages loading over 3 seconds are crawled less frequently") rather than vague generalisations
• Bold the key claim in each paragraph — this gives models a visual anchor and also helps human readers

LLMO vs GEO vs AEO: the practical overlap

These three terms describe overlapping but distinct concerns:

In practice, executing LLMO tactics well covers most of what GEO and AEO require. The practical difference is perspective: GEO asks "can AI systems find and retrieve my content?", AEO asks "can AI systems extract direct answers from my content?", and LLMO asks "once extracted, will the model attribute this content to my brand with confidence?"

For a deeper treatment of how GEO works and where it fits in the broader AI search landscape, see our article on Generative Engine Optimisation explained.

Measurement: how to track LLMO performance

LLMO performance is currently measured with the same tools as GEO/AEO, because the same signals apply:

• Manual citation audits: query your target topics in ChatGPT, Perplexity and Claude weekly. Record whether your brand is named in citations, not just whether your URL appears. Named attribution is the specific metric LLMO targets.
• GA4 referral sources: sessions from chatgpt.com, perplexity.ai, claude.ai and bing.com/chat are direct referral signals from AI citation.
• Google Search Console AI Overview filter: for Google-specific attribution, GSC's Performance report shows AI Overview impressions and clicks.
• Branded search growth: increasing branded queries in GSC is a secondary signal of growing AI attribution — people heard your brand name from an AI system and searched for it.

Attribution without brand naming (URL cited but business name not mentioned) is a partial LLMO outcome. Full LLMO success is the model citing your business by name in its answer, not just your URL as a source.