As generative AI technologies integrate into financial workflows, understanding user interactions in the private equity domain is vital for optimizing information retrieval and user experience. This study uses a mixed-methods approach to explore interaction patterns within a generative AI chatbot prototype. Key themes, sentiments, and search behaviors were identified by analyzing 12 user interviews and 825 generative AI query inputs. Findings reveal predominant user intents—company analysis and sector-specific dynamics/market research—highlighting the importance of expertise in shaping interaction dynamics. A comprehensive product design framework tailored to private equity-specific needs is proposed, including delivering robust prompting support, offering high-level market insights and in-depth company analyses, clearly citing sources, and transparently communicating timeframes to users. This research provides actionable insights into designing intuitive, effective generative AI interfaces, advancing their application in private equity and broader financial sectors such as venture capital, asset management, hedge funds, investing banking, and M&A.