Building practical AI agents and multimodal workflows.
About
AI engineer in Shenzhen, focused on LLM applications, agent workflows, multimodal generation, and RAG systems. I care about turning prompt experiments into reusable, observable, product-grade systems.
Built a staged LLM pipeline for script parsing, narrative slicing, scene analysis, and shot generation. The system moves storyboard generation from single-prompt output into a reusable workflow with cross-episode character and scene memory.
2025-2026 · Direction Lead
Short Drama Editing · Character Identity
Reframed the bottleneck from isolated highlight detection to cross-episode identity stability. The rebuilt path supports entity alignment, retrieval, scheduling, and cache reuse for more continuous generated edits.
2024 · Core Algorithm Engineer
Enterprise RAG & Safety Review
Designed hierarchical document chunking, multi-stage retrieval, dynamic threshold filtering, and prompt-assisted safety review. Internal tests reached about 90% recall and answer accuracy.
Multi-Agent Contract Protocol for structured file-based coordination between coding agents.
Python
Updated Apr 2026
Kotaemon ContributorTop contributor experience in an open-source RAG document chat project, alongside tools such as Open-OmniSearch and GraphRAG visualization work.
Knowledge Science, Engineering and Management (KSEM), 2021. · Conference paper
04
Awards
2nd PrizeLegal Case Retrieval Task, Challenge of AI in Law (CAIL) 20213rd PrizeInformation Extraction Task, Challenge of AI in Law (CAIL) 20213rd PrizeJudicial Examination Task, Challenge of AI in Law (CAIL) 20211st PrizeSubway Passenger Flow Prediction, Guangxi Collegiate AI Design Competition 20202nd PrizeCSI Index Prediction, Guangxi Collegiate AI Design Competition 2019