2024年中国GenAI技术栈市场报告

Investment Rating - The report does not explicitly state an investment rating for the GenAI technology stack market in China [1]. Core Insights - The report aims to clarify the market demand for the GenAI technology stack, analyze its core components, and assess the competitive landscape among various players in the industry [1][4]. - The GenAI technology stack is defined as a comprehensive environment and platform for developing and deploying generative AI applications, integrating various technologies and tools from model initialization to deployment [21][30]. - The evolution of the GenAI technology stack reflects a shift from fragmented tools to a more cohesive and specialized ecosystem, particularly since the advent of large models in 2018 [25][45]. Summary by Sections Section 1: Industry Overview - The GenAI technology stack connects hardware infrastructure with end-user interactions, providing a one-stop solution for developers to efficiently build, train, fine-tune, deploy, and maintain generative AI models [29][30]. - The importance of the GenAI technology stack is highlighted by its role in supporting developers in rapidly and efficiently constructing applications, addressing new challenges in large-scale and cross-platform deployment [34][49]. Section 2: Core Component Analysis - The construction of complete end-to-end GenAI applications involves complex modules and processes, including model preparation, tuning, service, and governance [40]. - RAG (Retrieval-Augmented Generation) enhances the accuracy and transparency of generative AI applications by integrating external authoritative data sources [54][66]. - Multi-Agent Systems (MAS) provide a distributed approach to solving complex tasks, improving robustness, fault tolerance, and flexibility [57][69]. - Prompt Engineering optimizes the design of prompts to guide models in generating expected outputs, significantly impacting the quality and relevance of model responses [61][88]. - MLOps (Machine Learning Operations) streamlines the deployment and maintenance of machine learning models, ensuring efficient collaboration and continuous improvement [100][109]. Section 3: User Considerations for Building GenAI Applications - High-quality model construction is essential for matching user business needs and enhancing content generation efficiency [130]. - Security and compliance optimization is crucial in addressing potential risks such as data privacy breaches and erroneous decision-making in generative AI applications [127][128].