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Pyramid priming

As explained in Introduction to Generative AI, Second Edition

Pyramid priming is a prompting technique that layers information from simple definitions toward increasingly complex analysis.

Pyramid priming structures a prompt by moving from simple definitions to examples and then to increasingly complex analysis. This scaffold helps models handle technical topics where prerequisite understanding matters. section 7.4

Climate-policy analysis

A prompt might ask: “Step 1: Define carbon neutrality. Step 2: List examples of country climate targets. Step 3: Analyze the economic impact of proposed regulations.” section 7.4

How Introduction to Generative AI, Second Edition covers it

How deep it goes
Defines the layering pattern and gives one example, without detailing implementation, evaluation, or limitations.
Examples
Uses carbon neutrality, country climate targets, and economic effects of proposed regulations as successive prompt layers.
What it assumes
Assumes a task with prerequisite concepts and teaches the scaffold from the definition stage onward.
What you can do after
Apply it by ordering a prompt from definitions through examples to analysis.
How it connects
Connects pyramid priming with least-to-most prompting, generated knowledge prompting, and prompt chaining.
Cover of Introduction to Generative AI, Second Edition
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