Human-LLM Collaborative Workflow for Knowledge Production: A Strength- Limitation Complementarity Framework

Large language models (LLMs) have become core auxiliary tools in knowledge sorting, standardized writing, document iteration, procedural optimization, software development and programming coding. However, most existing human-LLM applications adopt simple linear cooperation modes, which only list the independent capabilities of humans and models. Such superficial cooperation cannot explain the structural necessity of collaborative coupling, nor guarantee the stability and robustness of generated outputs. A core defect of current research lies in the lack of systematic task boundary division and complementary mechanism design: existing work fails to construct targeted workflow based on the intrinsic strengths and limitations of the two collaborating agents. To solve this problem, this paper proposes a three-layer iterative collaborative workflow framework for human-LLM knowledge production. Different from conventional descriptive studies, this research takes strength-limitation complementary offsetting as the core logical basis. The overall workflow is divided into three interconnected functional layers: the human-dominated driving layer, the LLM-centered execution-transformation layer, and the dual-agent quality-assurance layer. This framework clarifies the exclusive functional boundaries of humans and LLMs in the full workflow, and explains the internal mechanism of cognitive gain brought by collaborative coupling. Combined with practical application requirements, this paper summarizes three design principles and differentiates implementation logics in typical scenarios including scientific sorting, standardized writing, and iterative optimization. The framework is further illustrated through practical application scenarios, including scientific sorting, standardized writing, and iterative optimization. This lightweight collaborative paradigm offers a conceptual and potentially scalable structural approach to human-LLM cooperative knowledge production.