The course is titled "The Machine-First Commerce & Visibility Stack." It is designed for product leaders, growth teams, data architects, and founders who are building for AI-mediated markets. The curriculum moves beyond traditional SEO and user experience (UX) to focus on making products, brands, and data legible, trustworthy, and actionable for AI agents.
The course excludes non-durable topics like generic prompt engineering or pure content generation to focus on "epistemic utility," architecture, and governance,. It is structured into five distinct phases:
Phase I: Truth & Structure (The Foundation)
This phase focuses on defining reality so models can reason rather than guess.
• Module 1: Ontology Before Intelligence. This module posits that AI failures often stem from ambiguity rather than a lack of intelligence. It teaches "Entity Authority Engineering" to replace marketing language with machine-safe taxonomies and controlled vocabularies, ensuring that when an AI encounters a concept, it can trace it to a precise definition,.
• Module 2: Evidence Engineering. This module addresses "hallucination" by enforcing "epistemic clarity". It introduces the Tri-Layer model, which separates content into Authoritative Knowledge (facts), Observed Reality (inference), and Reported Experience (opinion). This structuring transforms content into "evidence graphs" that machines can safely cite.
• Module 3: Encoding Uncertainty. This module teaches that absolute confidence is a risk signal to AI. It instructs on how to use "TruthCalibrate" tools to encode confidence intervals and validity windows, establishing the principle that "sources that admit what they do not know are safer than sources that claim to know everything",.
Phase II: Reasoning & Action
This phase addresses how AI systems operate and execute tasks.
• Module 4: From UX to RX (Reasoning Experience). This module argues that AI agents do not use interfaces; they reason about systems. It shifts design from User Experience (UX) to Reasoning Experience (RX), replacing CRUD APIs with "IntentIO" interfaces that expose capabilities, constraints, and permissions directly to the machine,.
• Module 5: Action Safety & Refusal Engineering. As AI moves from answering questions to taking actions (e.g., booking or buying), this module focuses on preventing premature commitment. It teaches the use of state machines to gate actions and emphasizes "refusal engineering"—the ability of a system to say "no" when conditions for safe action are not met,.
Phase III: Visibility Inside Models
This phase explores the new growth channels that replace traditional search rankings.
• Module 6: AI Visibility ≠ SEO. This module explains that Large Language Models (LLMs) do not rank links but assemble answers based on judgment. It replaces metrics like impressions with Presence (was the brand retrieved?), Citation (was it treated as evidence?), and Influence (did it shape the answer?),.
• Module 7: Speak Bot, Not Browser. This module introduces llms.txt as a "machine briefing document" rather than a sitemap. It focuses on providing AI crawlers—which are optimized for extraction rather than discovery—with high-signal, concise documents regarding core truths and policies,.
Phase IV: Entity & Training Data Control
This phase focuses on how models understand brands through entities and community validation.
• Module 8: Entity Authority Engineering. Because LLMs think in "entity graphs" rather than pages, this module teaches how to shape a brand's identity within knowledge graphs. It addresses failure modes like entity ambiguity and misclassification, using structured signals and schema to ensure the model categorizes the brand correctly,.
• Module 9: Reddit & Community as Training Data. This module posits that AI systems trust human behavior and post-purchase validation (often found in forums like Reddit) more than marketing claims,. It covers how to extract structured validation from community data and avoid "negative imprinting",.
Phase V: Governance, Drift & Defensibility
The final phase focuses on maintaining system integrity over time.
• Module 10: Observability of Judgment. This module addresses the "invisible influence" of AI decision-making where traditional analytics (clicks/traffic) do not exist,. It teaches methods to reconstruct influence by simulating prompts and analyzing evidence usage.
• Module 11: Drift, Kill Switches & Audit Trails. This module manages "knowledge drift," where accurate data becomes incorrect over time. It mandates the implementation of kill switches and validity windows to ensure systems can stop themselves when data is outdated, ensuring legal and ethical defensibility,.
Final Outcome
Graduates of this course are equipped to design systems where they can measure influence inside models, prevent hallucinations, and safely enable agentic commerce. The overarching philosophy is that in an age of machine judgment, "trust belongs to systems that know when not to speak, not to act, and not to decide".
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