Core Insights - The article discusses the implementation of large models in game development, highlighting the challenges and advancements in AI coding tools, particularly in the context of complex game projects [2][3][4]. Group 1: AI Tools in Game Development - Numerous AI coding tools have emerged recently, but their participation in game project coding remains limited due to the complexity and flexibility of game business [2][4]. - A large-scale internal survey revealed that game developers spend more time on code understanding rather than code writing, indicating a need for better tools to facilitate this understanding [4][6]. Group 2: Challenges in Game Development - Three main challenges were identified: lack of clear technical documentation (30%), the complexity of game development pipelines compared to traditional web development, and slow testing and debugging processes [6][8]. - The game development process often leads to accumulated technical debt due to rushed timelines, which complicates the coding and debugging phases [6][8]. Group 3: Knowledge Engineering in Game Development - The company has developed a game development knowledge engineering system to improve code understanding and collaboration among different roles such as planning, art, and development [13][14]. - The knowledge system integrates structured and unstructured data, allowing for efficient retrieval and application of knowledge within the game development context [14][19]. Group 4: AI-Driven Code Generation and Review - A dual-end system was created to enhance code understanding, generation, and quality review, focusing on integrating AI capabilities into the existing development environment [8][11]. - AI-generated code accounted for 30% of the total code produced, with the system contributing approximately 5 million lines of code monthly across various projects [41][44]. Group 5: AI Code Review Process - The company has implemented a combination of traditional static code analysis and AI-driven code review to ensure quality control throughout the development process [44][45]. - The AI review process aims to identify low-level errors that could lead to significant operational issues, enhancing the overall quality of the code produced [45][46]. Group 6: Future Directions and Team Collaboration - The focus is on creating a cohesive team AI agent system that facilitates collaboration across different roles in game development, aiming to enhance efficiency and knowledge sharing [55][56]. - The upcoming AICon event will explore further applications of AI in business growth and development efficiency, featuring insights from industry experts [2][56].