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We are building a powerful algorithmic AI NLP prompt engineering system that intelligently taps the knowledge of the foundation model best suited in the area of user input. Users are greeted by a sleek and clean UI with dynamic features such as creative nudges, visual elements, and drag-and-drop features all working harmoniously as a guided meta-prompt optimization generation and recommendation system.

Research shows a simple sentence like "Here's the most relevant sentence" embedded in prompts can boost AI recall by 70%! This is ripe for exploration! I think there are many other areas where model performance can be improved with these types of response seeds. This system will employ top prompt engineering techniques such as the use of special characters preceding prompts and inclusion of performance enhancing sentences into the prompt.

The first step towards mass adoption and realizing the true power of systems that include large language models is ensuring that there is a well-defined user friendly interface between their output and user-defined input. Integrate this with advanced reasoning techniques, like chain-of-thought, into the prompting process, and users could tap into that power without even knowing it, leading to richer, more nuanced results and a much broader user base.