Generative Engine Optimization (GEO) Course

This lesson unpacks the core differences between traditional SEO and GEO, highlighting the shift from keyword-based rankings to contextual, AI-synthesized responses. Students will explore how generative engines parse and prioritize content, and why brands that optimize early for GEO are positioned to dominate the future of search visibility.

By the end of this lesson, students will understand the key differences between SEO and GEO, how LLMs process content, how visibility is earned in generative engines, and when SEO and GEO strategies overlap or diverge.

By the end of this lesson, students will understand the macroeconomic and tech forces behind GEO, shifting user behavior from search to generative systems, GEO’s impact on brand visibility and marketing, and its strategic importance for early adopters in the evolving digital landscape.

By examining the architectural components of these engines—from semantic embedding to contextual reasoning—students will gain critical insight into how knowledge is assembled, filtered, and presented by AI. This understanding is essential for any strategist, content creator, or marketer seeking visibility in the era of AI-mediated search.

This lesson unpacks Retrieval-Augmented Generation (RAG). Students will explore how this architecture bridges the gap between static language models and dynamic information retrieval, enabling generative engines to pull in real-time or domain-specific data from external sources.

This lesson explores the invisible gatekeeping systems that determine which content is selected—and which is ignored—by generative AI engines. As traditional SEO gives way to AI-driven synthesis, visibility is no longer a matter of rank but of inclusion in the model’s reasoning process.

Drawing on both technical architecture and narrative design, the lesson equips students to build content that isn’t just discoverable, but selectable—designed to be included, quoted, and trusted by generative engines like ChatGPT, Google SGE, Perplexity, and Claude. By the end, learners will possess a strategic blueprint for shaping content that thrives in AI-mediated knowledge ecosystems.

By the end of this lesson, students will understand how to analyze user intent in generative search, structure content for AI synthesis, and align it with informational, comparative, transactional, or exploratory goals—enhancing visibility in AI responses through contextual layering and content-to-intent audits.

By the end of this lesson, students will understand how generative engines synthesize responses, select sources for attribution, and cite content across platforms like ChatGPT, Perplexity, Claude, and Google SGE. They'll learn to map their content’s presence in AI outputs and apply strategies to boost citation and influence.

By the end of this lesson, students will understand how structured data and natural language formatting influence content visibility in generative search. They’ll learn to implement schema markup, optimize for machine readability, and format content to boost extractability, citation, and inclusion in AI-generated responses.

By the end of this lesson, students will define Strategic Text Sequences (STS), understand how AI tokenizes and retrieves content, and learn to craft, position, and optimize STS to boost visibility, extractability, and citation in generative engine outputs.

Formatting for summarization and citation in LLMs involves structuring content to align with how generative engines extract and attribute information. Techniques like bullet points, tables, Q&A blocks, and clear hierarchical headings enhance extractability, while dense, declarative language boosts citation potential—making formatting a vital lever for visibility in AI-generated responses.

This pivotal lesson offers a detailed and practical synthesis of 11 proven tactics for increasing visibility in AI-generated overviews, particularly within systems like Google SGE, Perplexity, and ChatGPT with browsing. These tactics serve as the strategic convergence point of all prior lessons—bridging content design, structure, and AI behavior modeling into a replicable visibility framework.

This module explores the high-impact practice of citation engineering—strategically shaping content to be selected, attributed, and amplified by generative AI platforms such as ChatGPT, Google Bard, and Bing Copilot.

This lesson covers how to optimize for voice search and how to craft content that aligns with the conversational intent models powering AI assistants, voice-enabled agents, and large language model interfaces.

This lesson discusses ranking manipulation and prompt injection. Drawing from emerging academic research and real-world testing, it covers the techniques, risks, and implications of influencing LLM outputs beyond conventional optimization.

This lesson takes students deep into the mechanics, implications, and countermeasures of preference manipulation attacks—a sophisticated and emerging class of adversarial strategies targeting large language models (LLMs).

This esson addresses the ethical boundaries and responsible GEO practices, providing a principled framework for operating within AI-driven search environments while maintaining transparency, integrity, and trust.

This lesson introduces students to intelligence-driven applications of GEO: auditing competitors’ presence within large language models (LLMs). It combines competitive research methodology with advanced prompt testing and visibility mapping to give practitioners a repeatable framework for understanding who is winning in generative environments—and why.

This lesson equips students with a methodical framework for reverse-engineering top-ranking content structures in LLM-driven environments. It combines linguistic analysis, structural modeling, and retrieval logic interpretation to demystify why certain content is consistently surfaced—and how to replicate its strategic patterns ethically.

This lesson provides a deeply strategic and academically rigorous exploration of brand positioning within AI-driven recommendations—an increasingly critical function in a world where users trust synthesized, AI-generated guidance as much as (or more than) traditional reviews or search results.

This lesson focuses on crafting high-E-E-A-T content (Experience, Expertise, Authoritativeness, and Trustworthiness) designed not just for human audiences, but optimized for inclusion, citation, and preferential selection by large language models (LLMs) across platforms such as ChatGPT, Bard, Bing Copilot, and Perplexity.

This lesson dives into the data-driven foundation of effective GEO strategy, showing how to use analytics—not speculation—to guide decisions, measure impact, and optimize continuously in AI-powered search environments.

his lesson explores the integration of social listening with GEO strategy to ensure content not only aligns with generative engine structures—but also responds to real-time audience needs, language patterns, and emerging intent. By grounding optimization in audience data, this approach makes GEO adaptive, empathetic, and insight-driven.

This module dives deep into the methodologies, technical constraints, and strategic opportunities associated with tracking referral traffic from large language models (LLMs) and AI engines—an emerging frontier in performance attribution and visibility analytics.

This module focuses on the emerging suite of tools and platforms available to help practitioners track, audit, and benchmark their performance in generative systems—specifically covering Perplexity rank checkers, Semrush’s AI visibility features, and ChatGPT prompt auditing methods.

This lesson introduces a formalized framework for benchmarking GEO performance, known as GEO-Bench. It equips students with the methodology, scoring dimensions, and implementation strategies needed to measure, compare, and improve the effectiveness of GEO efforts over time and against competitors.

My custom GPT for optimizing content for generative engines.