GEO (Generative Engine Optimization)
Table of Contents
Key aspects of GEO #
- Different from SEO: Traditional SEO aims to rank in a list of links; GEO aims to be cited or mentioned directly inside AI answers, even if no link is clicked.
- Focus on content: GEO prioritizes high-quality, factually correct, and semantically rich content that models can parse, trust, and reuse.
- Goal is brand visibility: The outcome is increased awareness and trust via brand mentions and citations in AI-generated responses.
- Measured differently: Success is measured by brand citations and presence within AI answers, plus secondary signals like branded search and prompt visibility.
How GEO works #
- Content is foundational: Create machine-readable, authoritative content (clear claims, citations, stats, structured data) that LLMs can rely on.
- AI models summarize and synthesize: Engines read pages and generate new answers; they triangulate across signals and citations.
- GEO influences the output: By aligning content with model preferences (sources, quotes, stats, authority), you raise your odds of being included.
- Example: Ask an LLM for the best noise-canceling headphones; it may cite Bose/Sony content if those pages are trusted, recent, and well-structured.
You can watch a primer video on how GEO works and why it matters.
Start here #
- Fundamentals: What GEO is and isn’t; differences from SEO and AEO; foundations that make content machine-credible.
- Strategy: Category entry points (CEPs), prompt families, asset planning, distribution (PR, reviews, communities).
- Implementation: Site structure, schema and llms.txt, content patterns that LLMs cite (comparisons, alternatives, stats, quotes), and AI readiness.
- Measure success: Share of search, buyer-intent share, prompt visibility index, conversational query conversion.
- Tools: Research, benchmarking, monitoring, and publishing aids.
Continue to the sections below for in-depth guidance.