Core Viewpoint - The article discusses the concept of Retrieval-Augmented Generation (RAG) and its significance in enhancing the accuracy and relevance of generative AI models by allowing them to access external data, thereby reducing instances of "hallucination" [1][6]. Group 1: RAG and Vectorization - RAG solutions enable generative AI models to retrieve data they were not originally trained on, improving the contextual accuracy of their responses [1]. - One of the best methods to implement RAG is through vectorization, which converts text, images, or other information into a numerical format for easier processing by computers [3][5]. - Semantic search, which relies on vectorization rather than keyword indexing, allows for more precise information retrieval by capturing underlying meanings [4][5]. Group 2: VaaS Implementation - FactSet has developed a platform called "Vectorization as a Service" (VaaS) that simplifies the process of storing and retrieving data for AI solutions, allowing employees to upload documents or connect to databases for quick vectorization [7][11]. - VaaS enables the creation of centralized knowledge bases, making it easier for teams to access and search through various company information sources [12]. - Since the launch of VaaS, employees have created hundreds of specialized knowledge bases, enhancing information discoverability and usage [12]. Group 3: Impact of VaaS - VaaS has automated the data preparation process for AI solutions, significantly increasing the number of tokens processed by the system since its launch in June 2024 [13][17]. - The centralized management of data through VaaS facilitates easier access and collaboration among employees while maintaining data flexibility [17]. - The rapid development of AI solutions makes it increasingly important for companies to invest time in developing robust DevOps solutions, which VaaS supports by empowering employees of all skill levels [20].

Core Viewpoint - In the digital age, APIs have become essential pillars for large language models (LLMs), generative AI, and data management systems like data warehouses and data lakes [1][3]. Group 1: Role of APIs in AI and Data Management - APIs enhance the capabilities of LLMs and generative AI by accessing various data sources, which is crucial for businesses looking to leverage AI without overhauling existing infrastructure [3]. - Gartner predicts that by 2027, 40% of generative AI solutions will feature multimodal capabilities, indicating the increasing complexity and maturity of these technologies [3]. - APIs serve as standardized interfaces for integrating structured, unstructured, and file-based data, allowing developers to efficiently handle diverse data formats [3]. Group 2: Importance of APIs in Retrieval-Augmented Generation (RAG) - In the RAG domain, APIs are vital for connecting AI models to external databases, ensuring that the information used is current and relevant [4]. - APIs enhance the accuracy and contextual awareness of AI model outputs by integrating external datasets into the response process [4]. - Conversational APIs facilitate seamless interaction between users and AI models, exemplified by FactSet's conversational API, which optimizes financial workflows and answers numerous natural language queries [4]. Group 3: Efficiency and Decision-Making - Conversational APIs significantly reduce the time spent on manual searches, improving work efficiency for financial services companies [7]. - The integration of packaged data with conversational APIs and AI partnerships simplifies the management of large datasets, enabling data-driven decision-making [7]. - AI-generated portfolio commentary can provide high-quality narrative content, analyzing systemic and unique risks while offering tailored explanations and trend analyses [7]. Group 4: Strategic Benefits of APIs - APIs transform independent systems into an integrated technological ecosystem, providing numerous advantages for financial companies [10]. - They enhance agility by enabling real-time data flows and insights, allowing companies to quickly adapt to market changes [10]. - APIs improve efficiency by reducing redundancy and streamlining operations, optimizing resource management [10]. - By accessing real-time data, APIs create personalized solutions, such as customized investment strategies, significantly boosting customer satisfaction and loyalty [10]. - APIs facilitate continuous updates and integration without major infrastructure changes, ensuring companies remain agile and resilient amid future technological advancements [10].

正是凭借这一卓越能力，API 可以将各个媒体类型与应用程序功能连接起来，确保生成式AI系统能够自 如运用复杂的数据输入。如此一来，开发人员就可以创建更具动态性和多功能性的应用程序，从容应对未 来多样化的数据需求。 特别是在检索增强生成(RAG)领域，API至关重要，它为人工智能模型开启了通向外部数据库的大门，确 保模型中使用的信息是最新且相关的。API直接将外部数据集成到AI模型的响应过程中，提升了模型生成 准确且具备上下文感知能力输出的能力。对话式API则充当了促进用户与AI模型之间无缝交互的接口。 在当今数字化时代，应用程序接口(API)已经成为大型语言模型(LLM)、生成式 AI 以及数据仓库和数据 湖等数据管理系统的重要支柱。 就LLM和生成式AI范畴而言，API能够访问各种数据源，增强了洞察生成和内容创作的能力。对于那些希 望在不颠覆现有基础设施的情况下利用 AI 的企业来说，这种能力至关重要。 高德纳咨询公司(Gartner)预计，到2027年，40%的生成式 AI 解决方案将具备多模态功能。多模态意味着 系统能够处理文本、图像、音频和视频等多种不同类型的数据，在这其中，API 的关键作用愈发凸显 ...

Core Viewpoint - Novo Nordisk has successfully integrated AI tools, specifically Anthropic's Claude, into the drafting of clinical research reports, significantly reducing the time and cost associated with this process [3][5]. Group 1: AI Integration and Efficiency - Novo Nordisk began testing AI models, including Claude 3.5 Sonnet, to assist in drafting regulatory documents for drug approvals [4]. - The use of AI has reduced the drafting time for clinical research reports from approximately 15 weeks to under 10 minutes [5]. - Previously, over 50 writers were involved in drafting these documents, but now only 3 human writers are needed with the assistance of Claude [5]. Group 2: Cost Implications - The annual expenditure on Claude is less than the salary of a single writer, indicating significant cost savings for the company [5]. - Although there are no current layoffs among writers, the company plans to reduce new hiring and reallocate saved resources to other departments [5]. Group 3: Error Reduction and Human Oversight - The strategic director noted a significant decrease in error rates when using AI for document drafting [3]. - A common method to reduce AI error rates, known as retrieval-augmented generation (RAG), is employed, where human experts provide feedback to improve AI outputs [5]. - This practice allows for the reuse of accurate definitions and descriptions in future documents, enhancing overall efficiency [5].