AEO/LLMO Content Optimisation Framework.
A practitioner’s framework for structuring B2B content so search engines and AI tools can extract it and cite it. It puts substance first and structure second, with a way to measure whether any of it actually worked.
The first version of this framework leaned hard on extractability mechanics, and this one reorders it around what the research and my own results say earns a citation: the substance comes first, the structure delivers it, and citation tracking checks the result. Every rule here came out of production work, first at a B2B SaaS company serving higher education and now at OpinionX, where the first citation-tracking baseline had the brand named in 38% of AI answers and cited as a source in 37%.
Two disciplines, one framework.
AEO and LLMO solve the same core problem from different angles: making your content the answer, whether it’s served in a search snippet or cited by an AI model.
Answer Engine Optimisation
Optimising content for featured snippets, People Also Ask boxes and zero-click search results, so your content is the answer Google shows directly without anyone needing to click through. That takes explicit definitions, structured data and content that meets search intent in formats a machine can parse.
Large Language Model Optimisation
Structuring content so AI tools (ChatGPT, Claude, Perplexity, Gemini) can extract and cite it when they generate answers, so your content becomes a source these models pull from and credit. That takes citation-worthy claims, named sourcing and substance a model can’t reconstruct without you.
Substance, then structure, then measurement.
Most AEO programmes run this backwards: they start with the formatting because it’s cheap and checkable, then wonder why the citations never come. The order below is the whole point of the framework, because the work that earns a citation is also the easiest to skip.
Substance: what earns the citation
The claims, data, sources and point of view that give an AI model a reason to cite you over ten interchangeable pages. Teams skip this part, so it goes first.
Structure: how it gets extracted
The formatting that lets a machine lift your substance cleanly: front-loaded answers, question headings, self-contained sections. It’s necessary and fast, and it’s second-order, because it delivers the substance and can’t stand in for it.
Measurement: whether it worked
Tracking actual AI citations instead of audit scores. A green audit tells you the structure is tidy and nothing about whether you’re being cited, which is the only outcome that pays.
Substance pillars that give a model a reason to cite you.
These are the moves with the biggest payoff, and the ones the popular playbook treats as optional. Every one needs you to have done the work: the research, a position you’ll stand behind, and the credible voices in your space quoted accurately. Schema and heading format don’t appear anywhere on this list.
Original argument & point of view
The piece says something a model can’t pull from the recycled takes everyone else publishes: a position on what works for whom, or something you saw in your own work. If the page only repeats what ten others already say, there’s no reason to cite this one.
Statistics & specific data
Concrete, attributed figures give a model a fact to lift. Adding statistics raised visibility in AI answers by about 31% in the Princeton GEO study, and a vague claim gives a model nothing worth pulling into its answer.
Named sources & quotations
Cite credible third parties by name and quote recognised voices accurately. Quotations from named sources were the strongest method the Princeton GEO study tested, lifting visibility by about 41%, and citing sources lifted it 115.1% for pages ranked fifth, so quoting other people’s authority builds your own.
Comparison tables & structured evidence
A table of approaches with their trade-offs hands a model something concrete to lift and attribute, and it reads as evidence rather than opinion. Wherever a page compares options in paragraphs, the table is usually the most useful edit on it.
Current, verifiable sourcing
Use sources that are recent and check out, because citation rates shift month to month and engines change, so dated or unverifiable claims lose credibility and age out of the answer set fast.
What the research says drives citation.
The most cited measurement of what moves AI citation is the Princeton GEO study (Aggarwal et al., presented at ACM KDD 2024), which tested nine ways of rewriting content across 10,000 queries on generative engines. The methods that won were substance-side, and that finding is why the framework runs in this order.
visibility lift from adding quotations from named sources, the strongest method tested
Princeton GEO study, KDD 2024lift from citing sources for pages ranked fifth, while the top-ranked page lost 30.3% on average
Princeton GEO study, via Search Engine Landkeyword stuffing, the only method that scored below the unmodified baseline on visibility
Princeton GEO study, KDD 2024Structure pillars that get out of the way.
I build every one of these into every piece, so this layer is about sequence: structure is fast and mechanical, and it’s second-order because it delivers the substance underneath it. A green checklist on thin content still won’t get you cited.
Explicit definitions
Define every key concept and term clearly at first use, so the definition stands alone if the sentence gets pulled out of context, with no assumed knowledge and nothing that only makes sense inside its paragraph.
Extractable summaries
The first 150 words hold a self-contained answer to the core question, substance included. If a snippet or an AI response grabs only the opening, the reader still gets the answer instead of a tease.
Question-format headings
Write H2s as the questions people actually ask, which maps them straight onto People Also Ask queries and tells a model what each section answers.
Structured FAQ sections
A self-contained FAQ block near the end, each answer concise and citable without the surrounding context. Mark it up with FAQ schema where it helps, but write it for the reader and the extract first, because Google stopped showing FAQ rich results for most sites in 2023.
Cross-reference layer
Pieces reference each other instead of repeating themselves, and the links build a knowledge graph that crawlers and models can follow to judge your depth on a topic.
The content blueprint.
How the two layers turn into a repeatable structure for every piece: the same documented ruleset, ordered so the substance decisions come before the formatting ones.
Substance: claim and evidence first
Before any formatting, decide the specific attributable claims this piece makes that nobody else is making, the data it cites and the named sources behind it.
Opening: extractable summary (first 150 words)
A self-contained answer to the core question, substance included, so a snippet or AI response that grabs only this block still gets the answer. No throat-clearing.
Body: question-format H2 sections
Each section answers a query people actually type, with definitions up front and every claim standing alone and attributed to its source within the content itself.
FAQ block: three or four self-contained Q&A pairs
Concise, citable out of context and ready for People Also Ask extraction. Each answer works if it’s the only part a model reads.
Cross-reference layer
Link related pieces to each other to build a knowledge graph crawlers and models can follow for topical authority. Reference instead of repeating.
Quality gate: verify, then track
Every claim traced to documentation, data or stakeholder confirmation before publish. After publish, track actual AI citations instead of audit completeness.
How it fits into content production.
The framework runs from the first step of the workflow, and it ends with the step most teams skip: checking whether you were actually cited.
Research & intent
Keyword and intent mapping, plus what AI models already say about the topic and where the gap is.
Substance
Decide the original claim, gather the data, line up named sources, build the comparison table. This is the part that earns the citation.
Structure
Front-load the answer, write headings as the questions people ask, and keep sections self-contained so they survive extraction.
Verify
Source log check: every claim traced to documentation, data or stakeholder confirmation before it ships.
Optimise
Meta elements, schema markup, internal cross-references, FAQ formatting, readability balance.
Measure
Track whether AI tools actually cite the piece, across a fixed set of buyer prompts run repeatedly, because that’s the scoreboard, whatever the audit score says.
What bad AEO looks like versus good.
“Add substance” is true and useless at the same time, so here are two versions of the same article. Both are well formatted, and only one of them says anything.
Bad AEO: structure on top of nothing
- Generic claims any competitor’s post also makes
- No data or named sources, and nothing specific
- Wrapped in FAQ schema and question-format headings
- Scores 95/100 on the audit
- Never cited, because it offers a model nothing it can’t get elsewhere
Good AEO: substance the structure delivers
- A specific, attributed claim: named study, percentage, year
- A recognised practitioner quoted by name
- A comparison table with trade-offs
- An argument about what works at which stage
- Clean structure carrying all of it, so there’s a reason to cite this page
Traditional SEO vs. AEO/LLMO content.
The difference is structural: content rebuilt so machines can extract value without a human reading the full page, and given something worth extracting once they do.
Traditional SEO content
- Keyword density as the primary signal
- Value depends on the click-through
- Definitions buried in context
- Stats with no visible sources
- Generic headings (“Benefits of X”)
- Content that exists in isolation
AEO/LLMO-optimised content
- Citability as the primary signal
- Zero-click value through direct answers
- Definitions up front and standalone
- Every claim attributed and verifiable
- Question-format headings matching the queries people type
- A cross-referenced knowledge graph
The ground has already shifted.
Discovery is moving off the blue link and into answers, which is why being citable now counts as much as ranking.
of B2B software buyers now start their research with an AI chatbot more often than with Google, up from 29% a year earlier
G2, April 2026of Google visits with an AI summary led to a click on a result, against 15% of visits without one
Pew Research Center, 2025Six mistakes that kill AI discoverability.
Most teams treat AEO as an add-on to their existing SEO workflow and run it in the wrong order. That’s the first problem, and here are the rest.
Doing the structure first
The intuitive order is to write the content, then bolt on schema, question headings and an FAQ. That sequence polishes the cheapest, most measurable layer and leaves the substance untouched, which is exactly backwards.
Make the substance decisions before and during writing, because nobody can quietly skip substance when it’s step one.
Trusting a green audit
Audits score what a script can check in milliseconds: schema validity, heading format, the presence of an FAQ block. None of that checks whether the content holds the attributed data and named sources that drive citation.
Measure citations instead of checkboxes. The audit is hygiene and citation tracking is the scoreboard, and confusing the two is how teams stay proud of a figure that tells them nothing.
Publishing what a model can reconstruct without you
If the prose says only what ten other pages say, no amount of formatting earns the citation. A model assembling an answer has no reason to reach for an interchangeable page.
Put something on the page a model can’t get anywhere else: original data, a named source, an argument with a position.
Orphan statistics and missing sources
A stat with no visible source is a liability, and skipping external citations leaves one of the strongest moves in the GEO study unused. If your “73% of buyers” has no link, a model has no reason to cite you over the five pages that attribute the same figure.
Attribute every figure, and cite credible third parties by name. External sourcing is a citation driver, and it belongs in the sentence, never in a footnote.
Burying definitions in narrative
When a key term first appears three paragraphs into an anecdote, no machine is extracting that definition, and if it only makes sense inside the paragraph it lives in, it’s invisible.
Define every key term at first use, in language that still works if the sentence is pulled out of context entirely.
Skipping verification
One fabricated stat or unverified timeline, or a guess presented as fact, and the whole piece loses its authority as models cross-reference claims across sources.
Keep a source log and trace every claim back to documentation, data or stakeholder confirmation before it goes live, because accuracy beats comprehensiveness.
Anyone selling you certainty on AEO is selling you something. The Princeton figures are from 2024, the tests ran on a subset of results rather than live Google rankings, and the engines have changed since. AI answers also shift from one run to the next: in a 2026 SparkToro and Gumshoe study, the tools returned the same list of brands fewer than 1 in 100 times, so treat the magnitudes as directional. What’s held up across every content type I’ve applied this to is the order: build the substance, structure it cleanly, then measure whether you’re actually being cited. I’m sure of that part, and the exact percentages are softer than the confident playbooks make them sound.