The Force Multiplier Index: Precision Phrases for AI Capability Amplification (v1.0)

The Force Multiplier Index is a codex of 20 engineered phrases across eight categories — Quality Elevation, Depth, Inversion, Synthesis, Compression, Leverage, Meta, and Temporal — each designed to unlock specific AI reasoning modes that default prompting does not reach. Each phrase includes deployment context, mechanism explanation, and the specific cognitive mode it activates. The index also includes a stacking protocol for compound amplification: sequencing multiple phrases to produce a compound rather than a linear improvement. The underlying principle: in the age of AI, what you know matters less than knowing how to ask. Default prompting accesses approximately 10% of a model's reasoning capability. These phrases access the rest. Companion to The Ceiling Protocol v4.0 (doi.org/10.5281/zenodo.19273431) and the Context-First AI Development methodology (doi.org/10.5281/zenodo.18842764).

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-03-28
DOI
https://doi.org/10.5281/zenodo.19274609
Primary Topic
AI-based Problem Solving and Planning
Type
article
Field-Weighted Citation Impact
0.00
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article

The Force Multiplier Index: Precision Phrases for AI Capability Amplification (v1.0)

James Edward Moore II
Zenodo (CERN European Organization for Nuclear Research)
AI-based Problem Solving and Planning
article

The Force Multiplier Index: Precision Phrases for AI Capability Amplification (v1.0)

James Edward Moore II
article en

Abstract

The Force Multiplier Index is a codex of 20 engineered phrases across eight categories — Quality Elevation, Depth, Inversion, Synthesis, Compression, Leverage, Meta, and Temporal — each designed to unlock specific AI reasoning modes that default prompting does not reach. Each phrase includes deployment context, mechanism explanation, and the specific cognitive mode it activates. The index also includes a stacking protocol for compound amplification: sequencing multiple phrases to produce a compound rather than a linear improvement. The underlying principle: in the age of AI, what you know matters less than knowing how to ask. Default prompting accesses approximately 10% of a model's reasoning capability. These phrases access the rest. Companion to The Ceiling Protocol v4.0 (doi.org/10.5281/zenodo.19273431) and the Context-First AI Development methodology (doi.org/10.5281/zenodo.18842764).

Zenodo (CERN European Organization for Nuclear Research)
Independent Electricity System Operator (CA), Agruicultural Research Institute (CY), Czech Academy of Sciences, Institute of Experimental Medicine (CZ)
Openalex Percentile: Top 4%
AI-based Problem Solving and Planning
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