AEO/LLMO Content Optimisation Framework.
A practitioner’s framework for structuring B2B content so search engines and AI systems can parse, extract, and cite it. Substance first, structure second, and a way to measure whether any of it actually worked.
The first version of this framework leaned hard on extractability mechanics. This version 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 tells you whether any of it worked. It’s based on real production workflows, not theory.
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. The goal: your content is the answer Google surfaces directly, without requiring a click-through. That takes explicit definitions, structured data, and content that meets search intent in parseable formats.
Large Language Model Optimisation
Structuring content so AI systems (ChatGPT, Claude, Perplexity, Gemini) can parse, extract, and cite it when generating answers. The goal: your content becomes a source these models pull from and attribute. That takes citation-worthy claims, named sourcing, and substance a model can’t reconstruct without you.
Substance, then structure, then measurement.
Most AEO programs 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 the work that’s 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. This is the part teams skip, 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. Necessary and fast, but second-order. It delivers the substance; it can’t replace it.
Measurement: whether it worked
Tracking actual AI citations, not audit scores. A green audit tells you the structure is tidy. It tells you nothing about whether you’re being cited, which is the only outcome that pays.
Substance pillars: give a model a reason.
These are the levers with the biggest payoff and the ones the popular playbook treats as optional. Every one requires that you’ve done the work: the research, a position you can defend, the credible voices in your space quoted accurately. None of it is schema or heading format.
Original argument & point of view
The piece says something a model can’t pull from the public soup of recycled takes: original observation, a position on what works for whom, something that came out of actual work. If the page only repeats what ten others already say, there’s no reason to cite this one.
Statistics & specific data
Concrete, attributed numbers move AI visibility more than any formatting change. Adding statistics improved citation likelihood by around 41% in the Princeton GEO study. Vague claims get skipped; specific ones get pulled into the answer.
Named sources & quotations
Cite credible third parties by name and quote recognised voices accurately. Citing external sources lifted visibility by up to 115% for lower-ranked content; quoting named sources by around 28%. Pointing at other people’s authority is how you build your own.
Comparison tables & structured evidence
Content with comparison tables earns more citations than text-only equivalents. 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.
Current, verifiable sourcing
Sources that are recent and check out. Citation rates shift month to month and engines change, so dated or unverifiable claims age out of the answer set fast. Recency is part of credibility.
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 content strategies across 10,000 queries on multiple generative engines. The strategies that won were substance-side, not structure-side. That finding is the spine of this framework’s ordering.
citation lift from citing external sources, for lower-ranked content
Princeton GEO study, KDD 2024adding more words did nothing; keyword stuffing scored below the baseline
Princeton GEO study, KDD 2024Structure pillars: get out of the way.
I build every one of these into every piece, so this isn’t a knock on structure. It’s about sequence. Structure is fast and mechanical, which is exactly why it’s second-order: it delivers the substance underneath it. A green checklist on thin content is a well-formatted way to stay invisible.
Explicit definitions
Every key concept and term defined clearly at first use, written to stand alone if the sentence gets pulled out of context entirely. No assumed knowledge, no definition that only makes sense inside its paragraph.
Extractable summaries
The first 150 words contain 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
H2s written as the questions people actually ask. This maps directly to People Also Ask queries and gives a model clear context about what each section answers.
Structured FAQ sections
A self-contained FAQ block near the end, each answer concise and citable without surrounding context, formatted for schema markup and zero-click extraction.
Cross-reference layer
Pieces reference each other instead of repeating themselves, building a linked knowledge graph that crawlers and models can traverse to assess your depth on a topic.
The content blueprint.
How the two layers translate into a repeatable structure applied to every piece. Same documented ruleset, reordered 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, definitions front-loaded, every claim standalone and attributed to its source within the content itself.
FAQ block: three self-contained Q&A pairs
Concise, citable out of context, formatted for schema markup and PAA extraction. Each answer works if it’s the only thing a model reads.
Cross-reference layer
Link related pieces to each other to build a knowledge graph crawlers and models can traverse 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 rather than 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. The part that earns the citation.
Structure
Front-load the answer, write headings as questions people actually ask, 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 systems actually cite the piece. That’s the scoreboard, not the audit score.
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. One of them said something.
Bad AEO: structure on top of nothing
- Generic claims any competitor’s post also makes
- No data, no named sources, 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 for 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 restructured 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 front-loaded and standalone
- Every claim attributed and verifiable
- Question-format headings matching 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 citability now matters as much as ranking.
predicted drop in traditional search volume by 2026
Gartner, 2024Six mistakes that kill AI discoverability.
Most teams approach AEO as an add-on to their existing SEO workflow, in the wrong order. That’s the first problem. 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 optimises the cheapest, most measurable layer and leaves the substance untouched, which is exactly backwards.
Make the substance decisions before and during writing, not after. When substance is the first step, it can’t be quietly skipped.
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 actually drive citation.
Measure citations, not checkboxes. The audit is hygiene; citation tracking is the scoreboard, and confusing the two is how teams stay proud of a number that doesn’t matter.
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 the single biggest lever 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 number.
Attribute every number, and cite credible third parties by name. External sourcing is a citation driver, not a footnote.
Burying definitions in narrative
When a key term first appears three paragraphs into an anecdote, no machine is extracting that definition. 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, one speculative claim presented as fact, one unverified timeline, and the whole piece loses its authority signal as models cross-reference claims across sources.
Keep a source log. Trace every claim back to documentation, data, or stakeholder confirmation before it goes live. Accuracy over comprehensiveness.
Anyone selling you certainty on AEO is selling you something. The Princeton numbers are from 2024, the engines have changed since, and citation rates move heavily month to month, so treat the magnitudes as directional rather than gospel. What has 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. That part I’m confident about. The exact percentages are softer than the confident playbooks suggest.