Our paper investigates effective methods for code generation in
"specific-domain" applications, including the use of Large Language Models
(LLMs) for data segmentation and renewal, as well as stimulating deeper
thinking in LLMs through prompt adjustments. Using a real company product as an
example, we provide user manuals, API documentation, and other data. The ideas
discussed in this paper help segment and then convert this data into semantic
vectors to better reflect their true positioning. Subsequently, user
requirements are transformed into vectors to retrieve the most relevant
content, achieving about 70% accuracy in simple to medium-complexity tasks
through various prompt techniques. This paper is the first to enhance
specific-domain code generation effectiveness from this perspective.
Additionally, we experiment with generating more scripts from a limited number
using llama2-based fine-tuning to test its effectiveness in professional domain
code generation. This is a challenging and promising field, and once achieved,
it will not only lead to breakthroughs in LLM development across multiple
industries but also enable LLMs to understand and learn any new knowledge
effectively.

该研究调查了代码生成在 “特定领域” 应用中的有效方法，包括使用大型语言模型（LLMs）进行数据分割和更新，以及通过提示调整刺激 LLMs 更深入思考。我们以一款真实的公司产品为例，提供了用户手册、API 文档和其他数据。本文所讨论的思想有助于将这些数据分割并转换为语义向量，以更好地反映它们的真实定位。随后，将用户需求转换为向量以检索最相关的内容，在简单到中等复杂的任务中通过各种提示技术实现约 70% 的准确率。本文首次从这个角度增强了特定领域的代码生成效果。此外，我们还通过使用 llama2 进行基于微调的有限脚本生成实验，测试其在专业领域代码生成中的有效性。这是一个具有挑战性和有希望的领域，一旦实现，它不仅将在多个行业中取得突破，而且还能够使 LLMs 有效地理解和学习任何新知识。