## 1 Introduction  
In recent years, Transformer-based models have taken a dominant position in the field of natural language processing (NLP) due to their outstanding performance [1; 2]. Prior to this, Recurrent Neural Networks (e.g., LSTM) were widely used to solve a variety of existing NLP problems [3; 4; 5]. However, Recurrent Neural Networks exhibit limitations in capturing long-distance dependencies within data sequences, particularly struggling with information at the beginning or end of texts, as well as with distant information [6]. Furthermore, its architecture does not effectively support the parallelization of training and inference processes, which poses a significant challenge in terms of computational resource requirements [7]. In contrast, the advent of the Transformer architecture has effectively overcome these issues. It was initially proposed as a sequence-to-sequence encoder-decoder model [8], with its advantages lying in the use of attention mechanisms to capture long-distance relationships in text, and its ability to easily achieve the parallelization of computations.  
With the development of more powerful GPUs and TPUs [9], it has become possible to create models with an increased number of parameters, allowing these models to achieve or even surpass human-level performance in an expanding array of tasks [10; 11; 12]. To make the model's responses more aligned with human needs, the development of the InstructGPT model employed a reinforcement learning from human feedback approach [13], which is particularly important for model fine-tuning.  
However, due to the characteristics of the encoder-decoder model in the Transformer architecture, large language models (LLMs) need to convert input data into fixed-length representations, which the decoder then uses to generate output. Despite the \"multi-head\"design of the Transformer architecture offering significant improvements in context and semantic understanding, its effectiveness remains limited when dealing with complex text problems and specific tasks. In particular, within specific vertical fields, to accomplish certain tasks, it necessitates preprocessing the input text to enhance the efficiency of the encoder and increase the accuracy of the generated text. For example, [PERSON] and colleagues, when utilizing ChatGPT-3.5-Turbo-0301 for zero-shot Text-to-SQL tasks, employed structured inputs and prompt engineering, effectively improving the success rate of SQL statement generation on the Spider test set [14]. Furthermore, to enhance the text generation effect, [PERSON] and colleagues combined an additional genetic algorithm with ChatGPT's natural language processing capabilities to generate news headlines, achieving notable results [15].  
Based on these considerations, we designed an input text quality evaluation algorithm, VFS (Vertical Field Score), which is divided into three parts: prompt word score, vertical industry relevance score, and text logicality score. By performing a weighted calculation of the scores from these three aspects, we obtained the total score. Through experimental testing, we found that higher-scoring inputs were able to achieve higher quality, more specialized output results. This provides strong support for enhancing the output effects of generative AI in vertical industries.