Generative Engine Optimization (GEO) is the discipline of optimizing content to earn selection and citation in answers produced by generative AI engines. It enforces clear entity definitions, machine-readable structure, and verifiable source citations to align with model retrieval and synthesis. It targets AI answer surfaces, including LLM chatbots and generative search results. Its objective is increased inclusion, accurate attribution, and recommendation frequency in generated answers.

The difference in scope between GEO and AI Search Optimization:

• GEO: Optimization specifically to earn inclusion, correct attribution, and recommendations inside generative answers (e.g., ChatGPT, Claude, Gemini, Perplexity). Core levers: entity clarity, prompt-surface optimization, machine-readable markup, citation hygiene, chunking for answer selection. Primary KPI: citation/share of voice in generated answers and attribution accuracy.

• AI Search Optimization (AISO): An umbrella discipline that includes GEO and optimization for broader AI retrieval surfaces: LLM chat, generative search, RAG systems, in-product semantic search, voice assistants, and agents. Adds data/architecture work: ontology and schema governance, embeddings strategy, content architecture, source-of-truth management, distribution, provenance, monitoring, and actionability. Primary KPIs: surface coverage, retrieval precision/recall, answer accuracy, task success, and latency across AI touchpoints.

Shortcut explanation:

• Use GEO when the goal is “win more LLM citations in answers.”

• Use AI Search Optimization when the goal is “be findable and reliable across all AI retrieval contexts,” including RAG and agent workflows.