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The guide outlines Baidu Search’s evolution—from early web search to a super‑app with AI‑driven, multi‑modal results—details its four product streams and strategic focus, and maps a product manager’s career from junior to expert, highlighting essential business insight, design, analysis, project leadership, and soft‑skill qualities.
The article surveys Baidu Search’s intelligent question‑answering system, tracing its evolution from feature‑engineered retrieval to large pre‑trained and generative models, and detailing hierarchical readers, multi‑teacher distillation, retrieval‑enhanced generation, and instruction decomposition as key techniques for delivering fast, accurate, citation‑rich answers.
Baidu's search vertical offline computing system evolved through four stages—from a fragmented pre‑2018 processing setup to a unified business framework, then serverless functions, and finally a data‑intelligent architecture with multi‑layer abstraction, graph and multi‑language engines, achieving 5‑10× efficiency gains and dramatically reducing failures.
Baidu’s Search Architecture team evolved compute management from static physical‑machine deployment to cloud‑native mixed deployment, employing a governance system that combines elastic containers, Service Mesh, and committee‑driven operations, while leveraging tidal scaling, container tiering, performance‑curve‑based VPA/HPA, and fine‑grained traffic scheduling to deliver cost‑effective, flexible, and performance‑driven resource allocation across both temporal and task‑type dimensions.
The Baidu Search Closed‑Door Technical Symposium, the first core technical forum hosted by Baidu’s Search Architecture Department, brings senior engineers and junior backend developers together to discuss semantic retrieval, data‑driven big‑data processing, and vertical search offline architecture, while offering limited‑capacity sessions, networking gifts, and travel subsidies.
At Baidu Search, a large‑scale short‑text clustering system was built using multi‑level semantic splitting, fine‑grained aggregation and error‑correction, evolving from v1.0 to v2.0, and now clusters 100 million queries with 95 % accuracy and 80 % recall within three days.