A practical generative engine optimization course for SEOs who want to get cited by ChatGPT, Gemini, and Perplexity.
Three recorded sessions cover the full GEO workflow from audit to reporting.
Session one shows you how to audit what AI tools currently know about your brand. You learn to spot citation gaps, identify entity coverage issues, and benchmark your LLM visibility. You also learn the difference between AI Search, AI Overviews, and Chat Modes so you know exactly what you are optimizing for.
Session two goes into tactics. It covers the three main LLM response types: training data, RAG, and deep-query systems. Each requires a different approach. The session also covers entity-based optimization, schema markup for AI, content structuring, and how to use the LLMs.txt file. There is specific guidance for enterprise brands, local businesses, and ecommerce sites.
Session three is about measurement. You get frameworks for tracking LLM visibility, mentions, and citations using cost-effective tools. It covers bot log monitoring, controlled GEO experiments, and reporting formats that work for clients and management.
What you will learn:
How to audit your brand's current AI footprint across ChatGPT, Gemini, Perplexity, and other LLMs.
How to optimize for training data, RAG, and deep-query response types.
How to use schema markup, entity signals, and content structure to increase AI citations.
How to track LLM visibility, brand mentions, and conversions using lean, cost-effective tools.
How to build reporting frameworks that show the value of GEO to clients and stakeholders.
Ideal for: Freelance SEOs, agency owners, and in-house SEOs who want a structured system for AI search optimization.