Conceptual Cartesian Mapping: Leveraging Large Language Models for Text Content Optimization and Idea Exploration
As we adapt to the digital era, there is a growing necessity for a strategic, scalable approach to idea generation and content optimization. Proove by DAC experimented with Conceptual Cartesian Mapping, a method where ideas are portrayed as points within a multidimensional space, with each axis representing a unique text attribute. We call this the Idea Space, which allows for systematic ideation, content creation, and controlled differentiation of ideas within set boundaries.
To aid this process, we leverage Large Language Models (LLMs), capable of interpreting or generating text with extraordinary precision. These LLMs can assess unstructured text blocks and grade them along various scales, enabling the generation of tailored content through a series of intricate instructions. Our initial findings from this experiment have been promising.
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