Search and AI Optimization: Systems, Psychology and Robots

Search and AI Optimization: Systems, Psychology and Robots teaches you how discovery actually works in the era of web crawlers and generative answer engines. You start with first principles: how search engines build indexes, how people formulate queries, and how organic discovery creates compounding value without paid media spend.

You then explore customer psychology and behavioral patterns, studying intent shifts, multi-step search paths, zero-click behavior, and cognitive friction. Next, you examine the technical mechanics of search robots: web crawlers, rendering budgets, JavaScript execution, HTTP status codes, canonicalization, and meta directives.

The course dedicates a full module to how AI answer engines (Perplexity, ChatGPT Search, Google AI Overviews) retrieve, summarize, and cite information through Retrieval-Augmented Generation, vector embeddings, and entity salience. You will learn to architect technical index hygiene, structured schema data, programmatic content systems, internal linking topologies, and editorial refresh loops.

Finally, you will study algorithmic loopholes, edge cases, parasite hosting, core update recoveries, and log analysis to measure bot behavior and defend brand citations at enterprise scale. 48 lessons in 8 modules, with detailed charts and workplace exercises throughout.