Design and Implementation of a RAG-Enhanced DevOps System for Community Trend Analysis

This study proposes an advanced data integration framework that automates the collection, analysis, translation, and verification of technical trends from major developer communities. In today’s rapidly evolving technological landscape, the ability to capture, analyze, and validate trends in real-time is critical for both organizations and developers. The proposed framework addresses the limitations of traditional methods, such as reliance on manual analysis or single-source data, by integrating retrieval-augmented generation (RAG), automated pipelines, and post-generation verification systems. Key advancements include ensuring data diversity through multi-channel data collection, improving the accuracy of trend identification with RAG-based context retrieval, enhancing translation precision of domain-specific terms via multilingual processing modules, implementing automated result verification through multi-layered quality assessments, and significantly reducing data processing time with automated workflows. This framework sets a new standard for trend analysis in dynamic environments, delivering actionable insights to developers and organizations with enhanced accuracy, speed, and reliability.