Learning Objectives
Formulate a comprehensive CMT-based prompting methodology with measurable evaluation criteria
Apply source-domain activation techniques to improve complex multi-step reasoning chains
Design reusable prompt templates based on metaphor coherence and structural mapping principles
Measure and rigorously compare CMT-prompted vs. standard LLM outputs across diverse task types
Applying Conceptual Metaphor Theory as a complete systematic paradigm for prompting modern LLMs.
This lesson builds on the theoretical foundations established earlier in the module and extends your understanding of how cognitive-linguistic principles apply directly to modern AI systems.
Learning Objectives
Formulate a comprehensive CMT-based prompting methodology with measurable evaluation criteria
Apply source-domain activation techniques to improve complex multi-step reasoning chains
Design reusable prompt templates based on metaphor coherence and structural mapping principles
Measure and rigorously compare CMT-prompted vs. standard LLM outputs across diverse task types
Key Concepts
Formulate a comprehensive CMT-based prompting methodology with measurable evaluation criteria
Apply source-domain activation techniques to improve complex multi-step reasoning chains
Design reusable prompt templates based on metaphor coherence and structural mapping principles
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