The development of Artificial Intelligence (AI) has led to significant advancements across numerous domains, including finance, healthcare, and customer service. Recent progress, particularly in the field of Natural Language Processing (NLP), has been driven by the emergence of Large Language Models (LLMs). These models utilize transformer architectures and vast datasets to perform a wide range of tasks, such as language translation, text generation, and complex data analysis. As AI technology continues to evolve, it offers the potential to streamline decision-making processes, enhance data management, and provide personalized recommendations. This study focuses on leveraging AI to address specific challenges in the financial sector. The objective of this paper is twofold: firstly, to develop a system capable of recommending bonds based on user-specific requirements, thus aiding investors in making informed decisions; and secondly, to collect bond data from sellers and integrate it seamlessly into an existing database. Through a systematic review of recent AI advancements and prompt engineering techniques, this paper aims to provide insights into how these technologies can be harnessed to improve financial data integration and recommendation systems.