Abstract: The construction of domain-specific question answering system frequently encounters challenges, including substantial data costs, intricate knowledge construction, and the significant differences among datasets from various domains. To address these challenges, an approach that integrates large language models and domain specific knowledge for question answering system construction is proposed. Most of the existing methods directly store and match local knowledge corpus in segments. When performing retrieval-augmented generation, the semantic matching between the query and the corpus is insufficient, thus reducing the quality of text generation. Therefore, the prompt aligned retrieval generation approach is proposed to unify the semantic space of user queries and corpus by generating pseudo question and answer pairs, thereby improving the retrieval efficiency of domain knowledge and the accuracy of answers. Experiments show that the proposed approach overcomes challenges related to high model training costs, enabling rapid deployment across various vertical domains and outperforming other methods.

Key words: vertical domain , large language model ,   question answering system ,   knowledge base