SEO, GEO, LLM-SEO — three disciplines, not three names for the same thing.

The decade between 2015 and 2025 turned SEO from a single discipline into a group of related but structurally distinct practices. Classical SEO has not disappeared — it has simply gained new neighbours that sit on the same brand content but work with a different architecture. For marketing leaders, heads of SEO and CMOs, cleanly separating the three disciplines — SEO, GEO, LLM-SEO — is the prerequisite for rational budget decisions and expectation management toward boards and stakeholders.

SEO — the established discipline

SEO, in the classical sense, optimizes a website to reach the highest possible positions in search-engine result lists for relevant queries. The unit of optimization is the document (the page), the unit of measurement is the ranking (position in the SERP), the dominant channel is Google (with Bing as second), and measurement runs through Search Console impressions, clicks, average position and traffic analytics.

The lever structure of classical SEO is largely stable: technical crawlability as the entry criterion, indexability, on-page signals (title, headings, keyword use, internal linking), authority signals (backlink profile, domain-level authority, topical authority), user signals (Core Web Vitals, engagement metrics), E-E-A-T signals (experience, expertise, authoritativeness, trustworthiness, especially for YMYL topics) and structured data for rich results.

What remains undiminished in classical SEO practice in 2026: technical hygiene (clean sitemap, fast load, mobile-friendly rendering), topical authority through content clusters, qualitative backlink profile from authoritative sources, E-E-A-T signal work with author entities. Rich results are no longer a stable lever: Google retired seven types in 2025, including ClaimReview, and has shown no FAQ rich results since May 7, 2026. What has also lost value: pure keyword density optimization (because modern retrieval systems work semantically), link building without a quality curator (because Google detects link spam ever more precisely), generic blog-output strategies without structural depth.

The primary SEO KPIs are well established and visible directly in GSC, GA4 and rank trackers: organic impressions, clicks, CTR, average ranking position, traffic value and conversions from organic traffic. For YMYL-relevant topics, rich-result coverage and Featured Snippet share complement the measurement.

What is GEO, and how does it differ from SEO?

GEO (Generative Engine Optimization) optimizes content to be cited as a source in the answers of generative search systems such as Google AI Overviews, AI Mode, ChatGPT search, Perplexity or Microsoft Copilot. SEO aims for a position in the results list; GEO aims for the citation of a passage.

The term was coined by Aggarwal et al. in the paper “GEO: Generative Engine Optimization” (KDD 2024). On their benchmark of 10,000 queries, targeted text changes raised visibility in generative answers by up to 40 percent. They measured how much of a source's text lands in the answer and how high up it appears. Adding source citations, quotations and statistics worked best. For AI Overviews and AI Mode, Google lists no additional requirements: standard SEO fundamentals apply, with no special AI text files or dedicated schema types. GEO does not replace SEO; it builds on it.

The unit of optimization shifts from the document to the passage (in our methodology: the 200–400 token chunk). The unit of measurement is the citation (a citation in a generative answer, not a ranking position). The dominant channels are multiple and run on their own indices. Based on public information and our tests (as of 2026): Google AIO uses Google's index, ChatGPT relies on Bing plus OpenAI's own search infrastructure, Perplexity and Claude use their own crawls or third-party indices, and Gemini sits inside Google's ecosystem; the exact backend assignments are not officially documented.

The technical basis of GEO is Retrieval Augmented Generation (see RAG & SEO). In RAG systems a user query is turned into a vector embedding, matched against indexed chunks, the top-N are reranked by a cross-encoder, and the language model synthesizes an answer from the best chunks with citation attribution. The levers shift accordingly: chunk-level embedding affinity instead of document-level ranking, claim-evidence pairing instead of keyword density, cross-encoder-reranking-friendly structure instead of general readability.

The specific GEO levers include passage engineering (see ChatGPT SEO), bot access control in robots.txt, multi-model monitoring through structured prompt matrices, and entity resolution via Wikidata and the Schema @id graph. Precision matters with bots: for visibility in AI search, OAI-SearchBot, Claude-SearchBot and PerplexityBot count; according to OpenAI and Anthropic, GPTBot and ClaudeBot collect training data, and according to Perplexity, PerplexityBot is not used for training. Google-Extended only controls Gemini training and grounding and has no effect on Google Search, and therefore none on AI Overviews. llms.txt, by contrast, is optional: a format proposed by Jeremy Howard in 2024 with limited adoption, which Google says it does not need. Not a core lever.

GEO-specific KPIs require their own measurement infrastructure: per-model citation rate, AI answer rate (the brand's position inside the answer), source-origin breakdown (own vs. third party vs. Wikipedia), share of voice against competitors, entity resolution rate, hallucination rate and fact drift. AI Overview and AI Mode impressions are now available in the GSC generative AI performance report (announced on June 3, 2026, rolled out to all websites worldwide as of August 31, 2026 according to Google; impressions only, no clicks). Citation rates in ChatGPT, Claude and Perplexity, as well as click and query data, still require dedicated instrumentation through LLM Citation Monitoring.

LLM-SEO — the narrowest sub-discipline

LLM-SEO is, strictly speaking, a subgroup of GEO that focuses specifically on the citation mechanics inside LLM interfaces — ChatGPT, Claude, Gemini, Perplexity. Unlike GEO, LLM-SEO does not include the AIO rendering layer or the Google search surface but focuses on the pure LLM chat experience.

In practice the terms GEO and LLM-SEO are often used interchangeably, which is acceptable in operational communication. In strategic documents and board presentations the distinction is valuable, however — because LLM-SEO work specifically addresses how brands are perceived in pure chat contexts (Claude without web access, ChatGPT without web search, enterprise Copilot scenarios) that typically rely on training data instead of live retrieval.

LLM-SEO levers overlap heavily with GEO levers, but with specific accents: training-corpus presence becomes more important than live indexation (since pure chat scenarios pull from training data), Wikipedia gains disproportionate weight (documented training mixes read it especially often: GPT-3 training passed over it 3.4 times versus 0.44 times for filtered Common Crawl), as does Wikidata as a freely reusable structured-data source, and bot access control for training crawlers (GPTBot, ClaudeBot, Google-Extended) carries specific weight.

LLM-SEO KPIs are a subset of GEO KPIs, with a focus on LLM chat scenarios: training-based entity resolution (when you ask ChatGPT without web search, does it return consistent biographical information about the brand?), consistent description across model versions, hallucination rate in pure chat scenarios, and confidence score in answers.

The structural lever overlap

Even though the three disciplines have different architectures and metrics, their optimization levers overlap considerably, by our estimate from project work 65 to 75 percent (an estimate, not a measured value). This shared layer is the most efficient working level: build the foundation cleanly and you serve all three disciplines without proportional additional effort.

The common foundation covers technical SEO hygiene (crawlability, indexability, Core Web Vitals), structured data with an @id graph (driving rich results, AIO citations and LLM entity resolution), entity consolidation via Wikidata and sameAs clusters, E-E-A-T signal work with author entities and corroboration, high-quality content with clear passage structure and claim-evidence pairing, and an internal link structure with topical coherence.

The remaining 25 to 35 percent is discipline-specific. For SEO: link building, classical on-page optimization, rich-result engineering. For GEO: bot access strategy, chunk-level embedding work, multi-model monitoring. For LLM-SEO: training-corpus visibility, entity consistency across model versions, hallucination prevention.

Budget allocation in practice

The most common strategic question: how should a brand split its 2026 budget between the three disciplines? There is no blanket answer — the right split depends on audience, industry and current maturity. For B2B software and enterprise brands a rough split of 50 percent classical SEO, 35 percent GEO and 15 percent specific LLM-SEO monitoring is a sensible starting point. For consumer retail and locally focused brands the weighting shifts more strongly toward classical SEO plus AEO features (Featured Snippets, voice search), with less weight on LLM-specific optimization.

Scale-ups with little established SEO base should not make the mistake of jumping straight into GEO before the SEO foundation is in place. Without indexation, without entity clarity, without a schema graph, GEO does not work either. The first 12 to 18 months should put 70 percent into classical SEO fundamentals, then gradually shift toward GEO.

Enterprise brands with a dominant SEO base face the inverse challenge: they risk continuing SEO work while marginal returns fall and neglecting GEO where marginal returns are substantial. A budget reallocation of 10 to 15 percent from SEO retention work into GEO build-out is often the rational decision in that situation.

Organizational consequences

The three-discipline structure has implications for team design and stakeholder communication. SEO teams built in 2020 typically have competence in classical SEO, often emerging competence in AEO features, and rarely deep competence in GEO-specific topics such as embedding optimization or bot access strategy. The organizational answer is not to replace the team but strategic upskilling plus external expertise to complement GEO topics during the build phase.

Board communication needs new KPI frameworks that introduce Answer Share of Voice as an aggregated meta KPI while keeping discipline-specific metrics transparent. A ranking report alone is no longer enough in 2026; board reports must reflect answer visibility across all three disciplines, with clear attribution of interventions to outcomes.

Conclusion: three disciplines, one foundation, integrated strategy

SEO, GEO and LLM-SEO are not interchangeable terms but three structurally distinct disciplines sharing a common root in search visibility. Keep them cleanly separate and you can allocate budget purposefully, measure KPIs precisely and keep expectations toward stakeholders consistent. Blur them and you produce misallocations and misunderstandings in board communication.

The practical recommendation is an integrated strategy with a clear shared foundation (technical, entity, E-E-A-T) plus discipline-specific emphases. A 50/35/15 budget split as a starting point for B2B, with adjustments by maturity and industry. Measurement through separate dashboards plus an aggregated Answer Share of Voice. Team structure with strategic upskilling instead of replacement. That is the structural answer that holds in 2026 and beyond — not the chase after the latest acronym.

Sources

• GEO: Generative Engine Optimization, Aggarwal et al., KDD 2024 (arXiv 2311.09735)
• AI features and your website, Google Search Central, accessed 2026
• Introducing Search Generative AI performance reports in Search Console, Google Search Central Blog, June 3, 2026 (updated August 31, 2026)
• Google’s common crawlers: Google-Extended, Google Search Central, accessed 2026
• Overview of OpenAI Crawlers, OpenAI, accessed 2026
• Does Anthropic crawl data from the web?, Anthropic, accessed 2026
• Perplexity Crawlers, Perplexity, accessed 2026
• The /llms.txt file, Jeremy Howard, llmstxt.org, 2024
• Language Models are Few-Shot Learners (GPT-3), Table 2.2, Brown et al., arXiv, 2020
• Simplifying the search results page, Google Search Central Blog, June 12, 2025
• FAQ (FAQPage) structured data: discontinued as of May 7, 2026, Google Search Central, 2026