独家洞察 | RAG如何提升人工智能准确性

Core Viewpoint - The accuracy of data is crucial for financial services companies utilizing Generative AI (GenAI) and Large Language Models (LLM), as inaccurate or low-quality data can adversely affect company strategy, operations, risk management, and compliance [1][3]. Group 1: Causes of Data Inaccuracy - Data inaccuracy in the financial services sector often arises from multiple factors, including the increasing volume and variety of data sourced from multiple vendors, patents, and third-party sources [4]. - "Hallucination" is a significant challenge in the financial sector regarding Generative AI, where models generate coherent but factually incorrect or misleading information due to their reliance on learned patterns from training data without factual verification [4]. Group 2: Importance of Retrieval-Augmented Generation (RAG) - RAG is a critical technology for improving the accuracy of Generative AI and significantly reducing hallucinations by integrating real data with generated responses [6]. - RAG combines the generative capabilities of LLMs with effective data retrieval systems, allowing for more accurate and contextually relevant answers, especially in financial risk assessments [6]. - RAG enhances the utilization of various data formats, enabling the processing of both structured and unstructured data efficiently, and connects existing legacy systems without the need for costly migrations or retraining of LLMs [7]. Group 3: Benefits of RAG - RAG helps address the main causes of data inaccuracy discussed earlier, providing more accurate answers based on proprietary data and reducing hallucinations [8]. - It allows for the integration of the latest knowledge and user permission management, ensuring that responses are based on up-to-date information [8].