  With the development of Large Language Models (LLM), numerous prompts have
been proposed, each with a rich set of features and their own merits. This
paper summarizes the prompt words for large language models (LLMs),
categorizing them into stimulating and framework types, and proposes an
Auto-Prompt Graphical Paradigm(APGP) that combines both stimulating and
framework prompts to enhance the problem-solving capabilities of LLMs across
multiple domains, then exemplifies it with a framework that adheres to this
paradigm. The framework involves automated prompt generation and consideration
of emotion-stimulus factors, guiding LLMs in problem abstraction, diversified
solutions generation, comprehensive optimization, and self-verification after
providing answers, ensuring solution accuracy. Compared to traditional stimuli
and framework prompts, this framework integrates the advantages of both by
adopting automated approaches inspired by APE work, overcoming the limitations
of manually designed prompts. Test results on the ruozhiba and BBH datasets
demonstrate that this framework can effectively improve the efficiency and
accuracy of LLMs in problem-solving, paving the way for new applications of
LLMs.
