Automated Data Labeling Pipeline with Reflection-based AI Agents
Text
ClassificationDeveloped a high-efficiency automated data processing pipeline utilizing LangChain and LlamaIndex to optimize AI training datasets. Key Innovation: Designed and implemented a Reflection-based Agent architecture that autonomously audits and corrects LLM-generated annotations. This closed-loop feedback system reduced manual labeling costs by approximately 60% while maintaining high accuracy. RAG Optimization: Integrated the Dify platform to build specialized knowledge base cleaning workflows. Performed semantic consistency checks and optimized data chunking strategies before vectorization to enhance RAG performance. Data Scope: Handled diverse datasets including complex backend code logic (Java/SQL) and IoT health monitoring indicators (Heart Rate/Blood Pressure) for elderly care systems. Quality Control: Implemented multi-stage validation gates ensuring that the final training data adhered to strict structural and semantic standards.