Core Insights - The integration of large models and enterprise knowledge bases is becoming essential for the financial industry to address challenges such as knowledge silos and high compliance costs [2][4][6] Group 1: Role of Knowledge Bases - In the context of large models, knowledge bases serve as a foundational infrastructure for enterprises, acting as a "textbook" that complements the "brain" of the large model [4] - The combination of a general large model and a specialized knowledge base allows for enhanced flexibility, rapid iteration, and controlled costs [4][5] Group 2: Financial Industry Requirements - The financial sector has heightened demands for knowledge base security and compliance due to regulatory requirements [6][7] - There is a strong emphasis on the accuracy and rigor of knowledge queries, as financial professionals deal with complex and rich data daily [7][8] - Financial institutions are increasingly interested in capturing individual employee knowledge to transform personal experience into organizational knowledge [7] Group 3: Data Security and Management - Data security is critical for financial enterprises, necessitating private deployment and comprehensive security measures across all infrastructure [8] - Effective management of knowledge within organizations requires multi-level authorization systems to control access to sensitive information [8] Group 4: Addressing AI Challenges - The phenomenon of "hallucination" in AI, where incorrect information is generated, poses a significant challenge [9][10] - Solutions to mitigate these issues include refusing to provide vague answers, ensuring traceability of outputs, and maintaining effective governance of internal knowledge [10][11] - The company has achieved over 92% accuracy in its AI responses through advanced document understanding and retrieval techniques [12]