Enhancing information extraction from long document: utilizing LLM's prompt engineering for long document set generation

Large Language Models (LLMs) face challenges such as outdated knowledge, generation illusions, and opaque reasoning processes. To address these challenges, Retrieval-Augmented Generation (RAG) has emerged as a solution by integrating external knowledge bases. However, traditional RAG encounters bottlenecks when dealing with long documents, such as information loss, incoherence, and low query precision at the granularity level. To tackle these issues, a novel method based on prompt engineering for efficient extraction of information from long documents is proposed. Firstly, a prompt engineering-based document segmentation algorithm is introduced to enhance the model’s capability to handle long document inputs. Then, utilizing prompt engineering, long documents are transformed into structured long document datasets with hierarchical summaries, including titles, headings, abstracts, and keywords, highlighting key information points to enhance document understanding and retrieval matching efficiency. Experimental results demonstrate that this method significantly improves the quality and precision of information extraction from long documents, outperforming traditional retrievalaugmented generation paradigms, thus paving the way for the development of interactive question-answering systems tailored for multi-document, multi-knowledge point scenarios. This method efficiently locates relevant content in long document sets based on query semantics, providing precise responses.