GLOSSARY
GEO (Generative Engine Optimization)
GEO is optimizing content to be cited by generative engines (ChatGPT, Perplexity, Google AI Overviews). The successor to SEO: the goal is no longer ranking first, but being the source the AI cites.
IN DEPTH
GEO (Generative Engine Optimization) refers to the practices that make content visible and citable by generative search engines: ChatGPT, Perplexity, Claude, Gemini and Google's AI Overviews. Where SEO optimizes for a position in a list of links, GEO optimizes for being the source quoted inside the AI-written answer. The main levers: direct, quotable answers at the top of content (TL;DR), sourced figures, clear structure (question-headings, FAQ), Schema.org structured data, and topical authority built through a coherent content network. For a recruitment agency or staffing firm, the stakes are growing fast: a rising share of executives look for providers and answers through AI assistants rather than Google. Being the cited source when a prospect asks "which ATS for a staffing firm" or "best IT recruitment agency" is the new ranking. The Cobalt blog applies these principles (quotable TL;DRs, structured FAQs, proprietary study data) across its content.
Frequently asked questions
SEO targets ranking in Google results (links); GEO targets citation inside AI answers (written text). The fundamentals overlap (quality, structure, authority), but GEO favors direct answers, sourced figures and quotable formats like TL;DRs and FAQs.
Because their clients and candidates now ask AI assistants their questions: "best IT agency in Lyon", "which ATS to choose". Being the source the AI cites on these queries generates qualified leads, exactly as Google's first position used to.
Related terms
- Agentic AIAgentic AI refers to AI systems able to pursue a goal autonomously: planning steps, using tools (ATS, email, calendar), executing actions and self-correcting. Generative AI answers, agentic AI acts.
- Vector Database (vector search)A vector database stores data (resumes, roles) as numerical vectors capturing their meaning. It enables semantic search: finding profiles by similarity of meaning rather than exact keywords.
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