CDT: A Cross-Dimensional Terminology Method for Precise AI Prompt Engineering

A systematic approach to defining AI prompts through cross-cultural, cross-temporal, and cross-domain validation

TL;DR

CDT (Cross-Dimensional Terminology) is a methodology for defining precise AI prompts by validating concepts across three dimensions: culture, time, and domain. When a concept has corresponding terms in both Eastern and Western philosophical traditions, it proves the concept is not arbitrary but has been deeply abstracted and historically validated.

The Problem: When AI Misunderstands Your Intent

Have you ever spent 30 minutes crafting an AI prompt, only to get results completely different from what you expected? This common frustration happens because:

You have a clear concept in mind but can't find the precise terminology
Vague words lead to AI misinterpretation
You spend excessive time revising prompts
Final results still diverge significantly from expectations

The root cause: We lack a systematic method to define and communicate abstract concepts to AI.

The Solution: Learning from Philosophical Traditions

Here's a key insight: When a concept has precise corresponding terms in both Eastern and Western philosophical traditions, it proves the concept captures fundamental patterns of human cognition that transcend cultural limitations.

Examples of Cross-Cultural Validation

Concept	Western Origin	Eastern Origin
Abstraction	Latin "abstractio" (to draw away)	Chinese "理" (li - inner principle)
Analysis	Greek "analysis" (to break apart)	Chinese "析" (xi - to divide and examine)

This cross-cultural correspondence tells us these concepts are universally understood across human civilization.

The Three-Dimensional Validation Framework

1. Cross-Cultural Validation

Verify whether a concept transcends specific cultural contexts:

Western tradition (typically from Greco-Latin origins)
Eastern tradition (typically from Chinese classical philosophy)

2. Cross-Temporal Validation

Examine a concept's evolution and stability through history:

Ancient original expressions
Medieval development and evolution
Modern precise correspondences

3. Cross-Domain Validation

Test whether a concept maintains consistent core meaning across different fields:

Philosophical original definition
Scientific professional application
Technical concrete implementation

How to Apply CDT: Step-by-Step Process

Identify Core Concepts: Determine key concepts that need precise expression in your prompt
Perform Cross-Cultural Term Matching: Find expressions in both Eastern and Western philosophical traditions
Extract Essential Features: Analyze common core characteristics across cultural terms
Find Modern Precise Correspondences: Based on extracted features, find precise modern language equivalents
Transform into Domain Terminology: Combine domain features with modern terminology
Apply in Prompts: Integrate precise terminology into your prompts

Practical Example: From Vague to Precise

Before CDT (Vague Prompt)

"I need AI to help me write code that looks nice, clean, using some common organizational methods..."

After CDT (Precise Prompt)

"I need AI to help me write high-quality code meeting these professional standards:

Apply appropriate software architecture patterns (e.g., MVC, layered architecture)

Follow code readability principles with consistent naming conventions

Maintain low coupling and high cohesion principles

Apply appropriate GoF design patterns for common programming problems"

Terminology Definition Micro-Format

To ensure AI accurately understands your defined terms, use this structured format:

Term: [term name]
Definition: [concise definition]
Features: [core characteristics, comma-separated]
Opposite: [contrasting concept]
Example: [brief example]

Example Application

Term: Systems Thinking
Definition: A way of thinking that analyzes interrelationships between elements and their collective effects
Features: Holistic, Relational, Dynamic, Networked
Opposite: Reductionist thinking (focusing only on isolated components)
Example: Understanding climate change requires considering complex interactions of multiple factors

Key Benefits of CDT

Benefit	Description
Universality	Concepts validated across cultures are more universally understood
Precision	Eliminates ambiguity and vagueness in terminology
Structure	Provides systematic terminology definition method
AI Comprehension	Dramatically improves AI's accuracy in understanding prompts

Conclusion

CDT transforms how we communicate with AI by grounding our terminology in concepts that have been validated across human civilization. When we master this method, we can express abstract concepts more naturally in the AI era, enabling machines to truly understand the essence of human thought.

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