Attention Engineering.
How the 2017 paper that powers ChatGPT, Claude, Gemini, and Perplexity quietly rewrote the rules of B2B search.
- The 5-layer attention engineering stack, translated from the original Transformer paper
- An 18-point AEO audit checklist you can run on any commercial page today
- The AI crawler access list most B2B sites are accidentally blocking
No credit card. Instant download. No drip sequence.
Five layers, drawn directly from how attention works.
Each layer is one mechanism inside the Transformer architecture, translated into a content rule and a checklist you can run today.
Direct Answer Openings
Attention rewards direct, factual sentences. Lead every page with a 40-word answer block, before brand, before hero copy.
Topic Density
Self-attention compares every token to every other one. Topic-dense pages outperform thin pages 5–10× in citation frequency.
Structural Hierarchy
Positional encoding makes order mathematical. H2/H3 hierarchy + schema is how you tell the model what's important.
Multi-Question Pages
Multi-head attention pulls multiple signals at once. One page should answer 3–5 related questions, not 1.
Top-of-Page Priority
Context windows are finite. Bury your best answer at line 800 and no model will ever cite it. Best paragraph in the first 200 words. Always.
Built across real B2B audits.
Every rule in this playbook was verified against live citation behaviour in ChatGPT, Perplexity, Gemini, and Google AI Overviews.
Stop guessing why ChatGPT skips you.
The playbook will not fix your AI visibility on its own. But you will not fix it without understanding what's in it.