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1. Fully Automated from Demand to Development: How to Build an AI Frontend Engineer Based on Multi-Agent Architecture
This article delves into the technical practices and core thoughts behind the Multi-Agent intelligent platform “Tiangong Wanxiang” developed by the Ant Financial frontend team.
2. From “Data Patching” to “Precise Judgment”: An In-Depth Analysis of the Key Role of Information Integrity in RAG Systems
Recently, during a project on intelligent defect duplication checking, an interesting problem arose. Despite using carefully designed prompts and a powerful LLM, the model still produced inconsistent “patched” results when returning duplicate defects. Through layered analysis, it was found that the root cause of the problem did not lie in the prompt engineering or the model itself, but in the “information gaps” within the RAG database. This document records the problem analysis and debugging process to share with everyone.
3. From Prompt to Context: Why is the Think Tool Inevitable for Formalization?
This article provides a solid theoretical foundation for current AI engineering practices (such as Prompt Engineering, Context Engineering, Think Tool) from the perspective of compilation principles. The evolution of AI programming does not come from nowhere; it reenacts the historical pursuit of formalization, verifiability, and reliability in software engineering.
4. Streaming Transcoding Testing Practices under Bilibili’s Third-Generation Transcoding System
Bilibili’s third-generation streaming transcoding system significantly reduces transcoding time through architectural optimizations (slice reuse, reduced IO overhead). The testing team designed a multi-dimensional assurance plan for the three core modules: scheduling, slicing, and transcoding. This includes functional link coverage of the entire process, strategy testing to verify performance and compatibility, and extreme scenario simulations (such as dynamic resolution and abnormal audio tracks) to ensure robustness, ultimately supporting the stable operation of millions of daily submissions.
5. Technical Exploration and Practice of a Self-Developed Customer Service Knowledge Base Based on TinyMCE Rich Text Editor | Dewu Technology
The customer service knowledge base is a system for centralized management and storage of information and resources related to customer service. Before the self-developed knowledge base went live, Dewu used a third-party knowledge base system. As the business developed, the issues related to knowledge maintenance volume and downstream system usage became increasingly apparent, while the current third-party procurement system struggled to meet the demands for efficient collaboration between internal systems. Based on these business needs, we developed a self-service customer service knowledge base.