Seeding the Training Layer
Chapter 7
Continued from Campaign Research: The Five Datasets That Decide GEO and AEO Strategy
Chapter 7 of 14 in LLM Mastery: How AI Recommends Brands for LLM Domination
The training layer is won by being consistently, repeatedly, factually present across the web the models learn from, before they learn it.
What does Chapter 7 argue?
The training layer is won by being consistently, repeatedly and factually present across the web the models learn from, before they learn it.
Training absorbs patterns, so the chapter's seeding programme publishes the same agreed facts, word for word, across many independent surfaces and formats, and keeps every one of them honest. Seeding done today surfaces at future training runs the reader does not control, which the chapter gives as the reason the layer is uncontested.
Glossary entries for terms it uses: Training layer and Retrieval layer.
What is in Chapter 7?
Chapter 7 works through two named parts:
- What survives into memory
- The seeding programme
How does Chapter 7 close?
Chapter 7 closes on this line:
Retrieval is renting visibility. Memory is owning it. Seed for the model that has not been trained yet.
Chapter 8, Winning the Retrieval Layer, follows it.