In this lesson you’ll cover:
- Why AI visibility is binary — you’re cited or invisible — versus the graded positions of traditional search
- How to measure your citation rate across ChatGPT, Claude, Perplexity, and Gemini, and set a baseline
- The five-stage retrieval pipeline: query understanding, candidate retrieval, semantic ranking, context loading, synthesis and attribution
- The four points where content fails the pipeline: discovery, relevance, size, and structure failures
- Entity clarity and salience — how models score entity importance, and why to name everything every time
- The four factors that drive citation probability: entity clarity, semantic match, verifiable facts with attribution, and structured data
- Dual-register writing (technical term plus plain-language equivalent) to close the vocabulary gap
- Optimizing for ranking (traffic) versus reuse (extraction), and how citation and ranking work together across the funnel
- The failure modes that keep good content from being cited, plus a diagnostic sequence and 4-week roadmap
- A concrete method and weighted formula for tracking citation performance over time