Large Language Model Enabled Industrial Document Generation Method Based on Retrieval Enhanced Prompt Learning

Industrial document is a critical component in manufacturing applications, such as maintenance and quality control. It requires extensive manual compilation, which is labor-intensive and fails to systematically encode complex relationships among large-scale documents. To address these challenges, this paper introduces a retrieval-enhanced prompt learning algorithm with large language model (LLM), including soft prompt, hard prompt, and retrieval-enhanced few-shot prompt. The proposed method first employs a Large Vision-Language Model (LVLM) to convert images into structured textual descriptions. Subsequently, a Large Embedding Model (LEM) converts the documents into a vector-based database, enabling efficient retrieval of task-relevant few-shot prompts. These retrieved prompts, combined with LoRA (Low-Rank Adaptation)-driven soft prompt and manually designed hard prompt, are fed into LLM to generate the final industrial document. Experiments are carried out on the dataset for the continuous casting equipment from the Bao Steel Company. The results demonstrate the framework’s superiority in automating document generation in terms of BLEU, ROUGE and Qwen-Score.