Search optimisation used to mean one thing: getting a page to rank on Google. Four labels now sit alongside the original. SEO is the foundation. AEO emerged when answer engines started returning one direct response instead of ten links. GEO emerged when generative AI systems started producing answers that cited source material. LLM SEO emerged when chat platforms became their own destination for category research and the work of optimising for a specific LLM became a discipline of its own.
The four are related but not interchangeable. Each one optimises for a different surface, a different output format, and a different measurement framework. Treating them as one bucket — or treating the newer three as replacements for the original — misses how the disciplines actually stack.
This article walks through each of the four, in roughly the order they emerged, explains what each one does and where it fits, and gives a working framework for deciding how much of each to invest in.
Key Takeaways
- The four disciplines stack rather than replace; SEO is the substrate, AEO and GEO sit above it, LLM SEO is the platform-specific layer at the top.
- AEO (answer engine optimisation) targets direct-answer surfaces: featured snippets, voice assistants, and structured-answer modules.
- LLM SEO is chat-platform-specific optimisation: tuning entity presence and content discoverability inside specific large language models like ChatGPT, Claude, Gemini, or Perplexity.
SEO: the foundational layer
SEO — search engine optimisation — is the original discipline, dating to the late 1990s and codified through the 2000s as Google’s algorithm became the dominant arbiter of web visibility. The work has three established pillars: technical SEO (crawlability, indexability, site speed, structured data), on-page SEO (content relevance to query intent, keyword targeting, internal linking), and off-page SEO (backlinks, domain authority, brand signals).
SEO has not been replaced by the newer disciplines. It is the substrate the newer disciplines build on. AI Overviews pull from indexed pages. Answer engines select from snippet candidates Google has already ranked. LLMs train on web content that traditional SEO has always shaped. A site with broken technical SEO will struggle on every newer surface, regardless of how much AEO or GEO work it does.
What changed in 2024-2026
The discipline has widened. SEO in 2026 includes content depth that LLMs reward, schema density that machine readers parse, and entity work that builds knowledge-graph clarity. The classic checklist still applies; it just now covers more surfaces than ten blue links.
AEO: answer engine optimisation
AEO emerged as a recognisable discipline around 2018-2020, driven by the rise of voice assistants (Alexa, Google Assistant, Siri) and Google’s expansion of featured snippets, knowledge panels, and structured-answer modules. The user’s input is a question; the surface returns one direct answer; the optimisation work is being that one answer.
AEO content is structured for selection: a direct answer in the first 1-2 sentences, FAQ blocks with proper schema, HowTo schema for procedural content, QAPage schema for question pages, and prose that holds up when the answer is read aloud rather than scanned visually.
Where AEO surfaces appear
Google featured snippets and People Also Ask blocks. Voice assistant responses on Alexa, Google Assistant, and Siri. Bing’s structured answers. Industry-specific Q&A surfaces in healthcare, finance, and other regulated categories. AEO work shows up wherever the search experience returns one direct answer instead of a list.
How AEO is measured
Featured-snippet ownership tracked across the target keyword set. Voice-answer wins for spoken queries. Structured-result appearances in SERP modules. Click-through impact, which is often negative for snippet wins (the answer is read on the SERP, not on the site) but positive for branded recognition.
GEO: generative engine optimisation
GEO emerged in 2023-2024 as generative AI systems started producing multi-paragraph answers with citations to source material. The user’s input is still a question, often a complex one; the surface returns a generated paragraph or page with cited sources; the optimisation work is being a cited source inside that generated answer.
The relevant surfaces are LLM-powered: ChatGPT search, Perplexity, Gemini, Claude (when web-enabled), Bing Copilot, and Google’s AI Overviews. The content needs to read as authoritative source material an LLM can lift cleanly — entity-defined, structured, dense with the kind of specific claims and data points that justify citation.
What GEO content looks like
Long-form prose with clear entity definitions, specific data points, named sources where appropriate, and structure that a parser can navigate. Schema density matters. Internal entity linking matters. Citation-worthy original analysis matters more than aggregated summary content, which LLMs increasingly skip in favour of original sources.
How GEO is measured
Brand mention count inside chat output across LLM platforms. Citation count when the brand’s URLs appear in generated answers. Share of voice in AI-generated responses for category-defining prompts. The dashboards for this are still maturing; manual prompt sampling remains a common method alongside emerging LLM monitoring tools.
LLM SEO: chat-platform-specific optimisation
LLM SEO is the newest of the four labels and the most platform-specific. Where GEO targets citation across the broad set of generative AI surfaces, LLM SEO tunes for a specific large language model — ChatGPT, Claude, Gemini, Perplexity, or others — based on that platform’s particular retrieval mechanism, knowledge cutoff, and content preferences.
The work includes entity presence inside the LLM’s training data (which sources does the model rely on for the brand’s category, and how is the brand represented in those sources), retrieval-augmented generation behaviour (which web sources the model fetches at query time), and platform-specific structured signals (some LLMs weight schema differently, some weight specific third-party authorities).
Why LLM SEO is its own discipline
The LLMs do not behave the same way. ChatGPT’s retrieval differs from Perplexity’s. Gemini’s grounding pulls heavily from Google’s index; Claude’s citations behave differently when web-enabled. A brand that is well represented inside one LLM’s answers may be absent from another’s. Optimising for one platform requires understanding that platform’s specific behaviour.
Where LLM SEO sits in the stack
LLM SEO is the platform-specific layer above GEO. GEO builds the foundation of citation-worthy content and entity clarity that benefits all LLMs. LLM SEO then tunes for the specific platforms that matter to the brand’s audience. For most brands, GEO is the larger investment and LLM SEO is the targeted top-up where category research concentrates on a particular platform.
How the four disciplines stack
The four are not competing approaches; they are layered.
Layer 1 — SEO. The foundation. Without crawlability, on-page relevance, and content depth, none of the higher-layer work compounds.
Layer 2 — AEO. Sits above SEO and reuses much of its content. AEO formats the answer concisely and adds the schema that direct-answer surfaces need.
Layer 3 — GEO. Sits above AEO and reuses the entity work and direct-answer formatting. GEO adds the citation-worthy depth and third-party distribution that generative surfaces reward.
Layer 4 — LLM SEO. Sits at the top, platform-specific. Tunes for the specific large language models the audience uses, based on each platform’s retrieval and citation behaviour.
The investment shape for most brands skews to layers 1-3, with LLM SEO added selectively where a particular platform dominates the audience’s research behaviour.
Conclusion
SEO, AEO, GEO, and LLM SEO are four labels for four layered disciplines, not four competing options. SEO is the foundation that every newer discipline builds on. AEO formats content for direct-answer surfaces. GEO engineers content for citation inside generated AI responses. LLM SEO tunes for the specific large language models a particular audience uses. The investment shape for most brands runs heaviest on the lower layers and lightest on the top, with LLM SEO added selectively.
The clearer the team is on which discipline does what, the easier it is to scope work and avoid the trap of treating all four as one bucket called AI SEO. The labels are imperfect, but the underlying differences are real, and the disciplines reward teams that keep them straight.
Frequently Asked Questions
What is the difference between SEO, AEO, GEO and LLM SEO?
Does AEO replace SEO?
What is the difference between GEO and LLM SEO?
Which discipline should I invest in first?
Is LLM SEO the same as GEO?
Why are there so many new SEO labels in 2026?
Will SEO still matter in five years?
If you want a clearer view of how these four layers map onto your category and audience before scoping any of them, enquire now.
