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The GEOly blog covers GEO (Generative Engine Optimization), AI search and agentic commerce — data-driven insights, benchmarks and playbooks on how brands get mentioned, cited and recommended across ChatGPT, Gemini, Perplexity and Google AI. Browse every topic by tag or author below.

Prompt engineering for SEO/GEO is the practice of researching the natural-language prompts customers type into AI engines and optimizing content so your brand becomes the answer — the AI-era successor to keyword research.

The Model Context Protocol (MCP) is the open standard that lets AI agents like Claude, ChatGPT, and Cursor connect to live external data and tools through one uniform interface — the plumbing that turns a brand from readable text into a callable system.

llms.txt is a proposed web standard — a curated Markdown index at your domain root that tells AI models which pages matter most; it takes an hour to ship, has no proven ranking weight in 2026, but hedges your brand for the era of live-browsing AI agents.

A brand knowledge graph is a structured map of your brand as an entity — nodes for facts, edges for relationships — that AI engines use to disambiguate, reason about, and recommend you; a weak entity never even enters the answer.

A grounding query is the machine-generated search an AI runs for itself before answering — the hidden retrieval layer that decides which pages get cited in AI Overviews, ChatGPT, Gemini, and Perplexity. Here is how they work and how to win them.

A GEO audit is a systematic evaluation of how easily AI engines can crawl, understand, cite, and recommend your brand — the diagnostic that replaces the SEO audit as buying decisions move into ChatGPT, Gemini, and Perplexity.