12 月 19～20 日的 AICon 北京站 将以 "探索 AI 应用边界" 为主题，聚焦企业级 Agent 落地、上 下文工程、AI 产品创新等多个热门方向，围绕企业如何通过大模型提升研发与业务运营效率的实 际应用案例，邀请来自头部企业、大厂以及明星创业公司的专家，带来一线的大模型实践经验和 前沿洞察。一起探索 AI 应用的更多可能，发掘 AI 驱动业务增长的新路径！ 以下是演讲实录（经 InfoQ 进行不改变原意的编辑整理）。 在游戏研发管线中，我们正在开展一系列与人工智能赋能相关的中台化工作，其中也包括与 AI 编程工具相关的内容。今天，我主要想和大家分享一下大模型在游戏研发中的落地实践，重点在 于我们在这一过程中的思考与感受。 理想与现实的差距 从早期的 ChatGPT 3.5 到 GitHub Copilot，再到 Devin、Claude Code 等，我们可以看到随着 大模型技术的不断发展，技术落地工作逐渐成熟，并催生了多种不同的产品形态。这引发了一个 令人焦虑的问题：随着这些看似无所不能的工具的出现，程序员是否真的处于失业的边缘？对于 从事 AI 编程的从业者来说，这种焦虑感尤为强烈，我们不 ...

Core Insights - Ant Group and Ningbo Bank's collaboration on the "Agentar Knowledge Engineering KBase" has been recognized as an exemplary case for international financial applications, showcasing its potential to enhance business intelligence in the financial sector [1] - The financial industry faces challenges related to "knowledge silos," where critical information is dispersed across different systems, leading to inefficiencies in service and consultation experiences [1] - The Agentar platform integrates knowledge processing management, logical reasoning engines, and intelligent application scenarios to provide a robust decision-making system for financial institutions [1] Technology and Implementation - The platform manages multi-source heterogeneous data throughout its lifecycle and features capabilities such as intelligent Q&A, knowledge processing, multi-route recall, and knowledge management [2] - A significant technological breakthrough is the knowledge-enhanced generation engine, which utilizes a collaborative mechanism of "planning-retrieval-reasoning" to improve knowledge quality through bidirectional indexing of knowledge graphs and raw text [2] - The system has transitioned from "fuzzy matching" to "precise reasoning," increasing reasoning depth from traditional 1-hop to 3-5 hops, enabling AI to understand financial knowledge and exhibit human-like logical reasoning [2] Performance Metrics - The solution has been implemented across various internal scenarios at Ningbo Bank, including market analysis, product interpretation, dialogue practice, and report writing [2] - Evaluation results indicate that the accuracy of complex Q&A has improved from 68% to 91%, with response times reaching the millisecond level [2] - Content recommendation accuracy has increased by 35%, and recall rates have improved by 40%, leading to a significant enhancement in business efficiency [2] Future Directions - Ant Group and Ningbo Bank plan to deepen their collaboration by expanding the technology to a broader range of financial business scenarios [2] - The partnership aims to actively participate in industry standardization efforts, promoting the regulated and large-scale application of knowledge engineering and large model technologies in the financial sector [2]