What is Generative Engine Optimization (GEO)? A new definition and differentiation

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The digital landscape is undergoing a silent revolution. As AI-powered platforms like ChatGPT, Google’s AI Overviews, Perplexity, Gemini, and Microsoft Copilot increasingly mediate how users discover information, traditional SEO tactics alone no longer secure digital visibility.

This is where Generative Engine Optimization (GEO) comes in — the practice of making content, brands, and data discoverable, citable, and actionable within AI-generated answers.

GEO is the intersection of SEO, PR, Branding and Product-Management. Depending on the objective, the focus shifts between these disciplines. Broadly, GEO can be divided into three sub-areas that differ clearly in objective, ownership, and target audience: LLM-Readability-Optimization, Brand Context Optimization, and Agentic Commerce Optimization (ACO).

Area 1: LLM-Readability-Optimization (Getting Your Content Cited)

Citation Optimization focuses on making your content the source AI systems retrieve, extract, and reference in their generated answers. This area is closest to classic SEO but adds new layers driven by how large language models (LLMs) actually work.

Key Technical Foundations

Retrieval-Augmented Generation (RAG): The backbone of most AI search systems. RAG retrieves relevant content passages from an index and uses an LLM to synthesize an answer, grounding responses in current, external data.
Query Fan-Out: AI systems decompose a single user query into multiple sub-queries. Your content must anticipate these expansions and cover multiple latent intents — not just one keyword.
Passage-Based Retrieval: AI systems select information at the chunk level , not the page level. Each information unit must stand on its own.

Optimization Levers

LLM Readability: Structure content clearly with short paragraphs, consistent terminology, and self-contained chunks (definitions, step lists, comparison tables).
Chunk Relevance: Each passage should be extractable, evidence-dense, scoped, authoritative, and fresh.
Multimodal Parity: Provide information in text, tables, images, videos, transcripts, and structured data — because AI routing decisions are modality-aware.
Topic Hubs & Intent Coverage: Build content that answers every branch of a query fan-out.

Area 2: Brand Context Optimization (Being Mentioned & Recommended)

While Citation Optimization is about linking to your content, Brand Context Optimization ensures your brand, company, or products are mentioned or recommended in AI-generated responses — even when no direct link is provided.

Why It Matters

The conversational nature of AI tools makes responses sound opinionated and authoritative. If LLMs base replies on false, outdated, or selective information about your brand, this easily comes across as fact — putting reputations at risk. And critically: visibility in LLMs can’t be bought. There’s no PPC equivalent. Up to 90% of citations driving brand visibility in LLMs can come from earned media.

Three Key Stages (per Edelman’s framework)

Know What AI Is Saying: Develop a baseline report to see inside the “black box” — how AI platforms describe your brand, competitors, and market. Break the data down by focus area, since a brand may rank well overall but be ignored in specific categories.
Develop Strategic Content Frameworks: Build a targeted content strategy that combines earned media, SEO, and crisis/risk mitigation to elevate the right messages.
Execute and Optimize at Scale: Track performance, learn what works, and widen application across topic areas.

Area 3: Agentic Commerce Optimization (ACO) — Being Selected by AI Agents

The newest GEO sub-discipline focuses on the individual product. With ChatGPT Instant Checkout, Google AI Mode, Perplexity Shopping, Amazon Rufus, and Microsoft Copilot, AI agents increasingly take over the role humans used to play: searching offers, comparing prices, checking availability — and sometimes making purchase decisions without a human ever seeing a product detail page (PDP).

The Core Shift

Visibility in a results list is no longer enough. A product can be beautifully SEO-optimized — but if the underlying data can’t be unambiguously parsed by a machine, the agent simply won’t consider it. Machine selectability and orderability become the actual goal.

ACO is closer to data management than classic marketing: the PDP and product feed become the interface between shop and machine.

The Agentic Readiness Ladder

Layer 1 – Citations (Inventory): Will your content be retrieved as a source? Most current GEO work focuses here.
Layer 2 – Reasoning (Operability): Can the AI extract and relate your facts accurately? Requires structured data, entity pages, and machine-readable relationship maps.
Layer 3 – Actions (Agentic): Can an autonomous agent actually execute a task — complete a transaction — using your data? Requires robust action endpoints.

ACO Content Evolution

Product descriptions shift from emotional sales rhetoric to fact-dense, context-rich data, including:

Use-case contexts
Problem-solution links
Conversational attributes (popularity ranks, structured relationship types)
Clear dimensions, materials, and specifications

Bringing It All Together

Generative Engine Optimization is not a single tactic but a multidimensional practice:

Area	Focus	Ownership
Citation Optimization	Getting content retrieved & cited	SEO / Content
Brand Context Optimization	Being mentioned & recommended	PR / Brand / Comms
Agentic Commerce Optimization	Being selected & transacted by AI agents	Product Data / E-commerce

Success requires a cross-functional approach. The winners will think like data providers as much as publishers — designing content and product data for integration into AI answers and agent actions, not just for stand-alone consumption.

The rules of the game are still being written. But those who understand the underlying architecture of AI search systems (RAG, query fan-out, chunk retrieval, entity reasoning) and act early will gain a significant advantage in the next iteration of digital visibility.

About the Author
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About Olaf Kopp

Olaf Kopp is an online marketing expert for Generative Engine Optimization (GEO) and SEO. He has over 15 years of experience in Google Ads, SEO, and content marketing. Olaf Kopp is one of the early pioneers in the fields of Generative Engine Optimization (GEO) and digital brand building, and the inventor of modern GEO and marketing concepts such as LLM readability, brand context optimization, and digital authority management. Olaf Kopp is Co-Founder, Chief Business Development Officer (CBDO) and Head of SEO & AI Search (GEO) at Aufgesang GmbH. He is an internationally recognized industry expert in semantic SEO, E-E-A-T, LLMO & Generative Engine Optimization (GEO), AI- and modern search engine technology, content marketing and customer journey management. Olaf Kopp is one of the first pioneers worldwide to have demonstrably worked on the topics of Generative Engine Optimization (GEO) and Large Language Model Optimization (LLMO). His first publications date back to 2023. As an author, Olaf Kopp writes for national and international magazines such as Search Engine Land, t3n, Website Boosting, Hubspot, Sistrix, Oncrawl, Searchmetrics, Upload … . In 2022 he was Top contributor for Search Engine Land. In addition, Olaf Kopp is a speaker for SEO, GEO and digital brand building at SMX, SERP Conf., SEO Vibes, OMT, OMX, Campixx...