Research key capabilities leading to AGI, track the latest academic and industry research achievements in LLM, and bring new technical ideas and methods to the business.
Leverage generative AI Agentic frameworks (e.g., Llama-Index, AutoGen, Semantic Kernel, Langchain), workflow frameworks (e.g, Langgraph), and trace logging (eg. Langsmith, wandb, phoenix) to develop robust AI features.
Deeply engage in key tasks such as AI Assistant data construction, LLM pre-training, fine-tuning, algorithm optimization, and model deployment.
Research key technologies for the implementation of LLM such as RAG and Agent, explore a new generation AI Assistant architecture based on LLM, and continuously enhance AI Assistant question-answering effectiveness and user experience.