Generative Engine Optimisation extends AEO into the specific challenge of getting cited by the large language models that now power B2B buyer research. Where AEO focuses primarily on Google's AI Overview system, GEO addresses a broader and faster-moving landscape: Perplexity, ChatGPT, Claude, Gemini, and the AI-powered search layers being built into every major platform. Understanding how these systems select and credit sources — and what makes content citation-worthy in their specific contexts — is the substance of GEO strategy.
What is Generative Engine Optimisation?
Generative Engine Optimisation (GEO) is the discipline of making content visible, credible, and citable within AI-generated responses from large language model systems. The term was introduced in academic research at Princeton in 2023 to describe optimisation strategies specific to generative AI systems as distinct from traditional search engines. For B2B marketers, GEO is practically important because buyers are now using these systems — not just as search supplements but as primary research tools — to understand categories, build shortlists, and evaluate vendors.
AEO vs GEO: the distinction that matters
AEO and GEO overlap substantially but serve different systems with meaningfully different citation behaviours. Conflating them produces strategies that are optimised for one context while missing the other.
Primary system
Google AI Overviews. Also applies to Bing Copilot and similar search-integrated AI layers.
Citation mechanism
Real-time web retrieval. The AI searches the live web and cites pages that match the query at the point of search. Freshness, crawlability, and structured data have direct impact.
Key signals
FAQPage schema, organic ranking position, content freshness, site crawlability, structured Q&A format.
Primary systems
Perplexity, ChatGPT (with web access), Claude (with web access), Gemini, and AI layers in LinkedIn, Salesforce, HubSpot, and other enterprise tools.
Citation mechanism
Mixture of training data representation and real-time retrieval depending on the system. Perplexity retrieves in real time; base ChatGPT draws on training data; Claude and Gemini vary by configuration.
Key signals
All AEO signals plus: training data representation (requiring long-established brand presence), entity clarity, authoritative third-party citation (being cited by sources that AI systems already trust).
How LLMs select sources to cite
The citation selection process differs between retrieval-augmented systems (like Perplexity) and parametric systems (like base ChatGPT). Understanding the difference determines where to focus GEO investment.
These systems query the live web at the point of a user's question and select sources based on: query relevance (does this page directly address the question?), content authority (does the domain have established authority signals?), content extractability (is the answer clearly structured so the AI can pull it cleanly?), and freshness (for time-sensitive queries, is this recent?). For retrieval systems, AEO and GEO signals are nearly identical — schema, structure, topical authority, and freshness all apply directly.
These systems draw on training data and cannot retrieve live content. Citation here means being present in the training corpus — which requires the brand and content to have been present, authoritative, and extensively linked before the training cutoff. For parametric systems, the GEO strategy is a long game: building brand entity recognition, earning citations from authoritative sources, and maintaining consistent publication. There is no shortcut.
Most major AI platforms are moving toward hybrid architectures — parametric knowledge supplemented by real-time retrieval for specific query types. Perplexity is retrieval-first; Gemini 1.5 blends both; Claude in web-enabled configurations retrieves selectively. For B2B marketers, the practical implication is to optimise for retrieval-based signals (the ones you can act on now) while building the long-term brand entity signals that affect parametric representation over time.
The GEO signals that matter for B2B
Based on emerging research and practical programme data, these are the content and technical signals with the most measurable impact on generative AI citation in B2B contexts.
GEO-driven content strategy
GEO does not require a separate content strategy from AEO. The same content architecture that serves AEO serves GEO — with specific additions that address the parametric and entity-recognition requirements of generative AI systems.
Research reports with a named methodology, specific sample sizes, and attributed findings are cited more reliably than editorial claims. "According to Harmonic London's 2026 B2B Buyer Behaviour Survey (n=312)" is citation-ready. "Research suggests" is not. Name your methodology and describe your sample explicitly.
LLMs are frequently asked "What is X?" for your category and adjacent categories. A brand that owns the authoritative definition of its category — clearly written, extensively linked, schema-marked — will be cited every time a buyer asks that question. Definition pages are undervalued relative to their GEO impact.
A named, proprietary framework — "The Three-Track Demand Generation Model", "The Buying Committee Engagement Framework" — becomes a citable entity. LLMs that encounter the framework name in multiple sources start attributing it to your brand. Named frameworks also earn organic links and shares in ways that generic content does not.
Content that aggregates publicly available information — "10 B2B marketing statistics for 2026" drawn from other sources — provides no original citation value and competes with thousands of similar pages. LLMs can synthesise this type of content themselves and have no reason to cite a specific source for it. Original perspective and original data are the only defensible GEO content investments.
Measuring GEO performance
- Monthly query testing: Run your 20 highest-priority target queries through Perplexity, ChatGPT (web-enabled), and Google AI Overviews. Record whether your domain is cited, for which queries, and in what context. Track this as a trend — growing citation frequency is the leading indicator of growing GEO authority.
- Self-reported attribution: "How did you first hear about us?" on every form and discovery call captures buyers who arrived via AI research channels that leave no digital footprint. This is currently the only way to attribute the parametric AI citation that affects brand awareness without trackable web traffic.
- Brand search volume trends: Rising branded search volume — tracked in Google Search Console — correlates with growing brand recognition from AI citations and other dark funnel channels. A buyer who encountered your brand in a Perplexity answer and subsequently searched your name directly contributes to this metric.
- Third-party citation monitoring: Track when your brand or content is cited by industry publications, analyst reports, and other authoritative sources. These citations directly improve parametric LLM representation and are measurable through media monitoring tools (Mention, Brandwatch, Google Alerts).