A Survey of Context Engineering for Large Language Models
Authors: name: Lingrui Mei, name: Jiayu Yao, name: Yuyao Ge, name: Yiwei Wang, name: Baolong Bi, name: Yujun Cai, name: Jiazhi Liu, name: Mingyu Li, name: Zhong-Zhi Li, name: Duzhen Zhang, name: Chenlin Zhou, name: Jiayi Mao, name: Tianze Xia, name: Jiafeng Guo, name: Shenghua Liu

The performance of Large Language Models (LLMs) is fundamentally determined
by the contextual information provided during inference. This survey introduces
Context Engineering, a formal discipline that transcends simple prompt design
to encompass the systematic optimization of information payloads for LLMs. We
present a comprehensive taxonomy decomposing Context Engineering into its
foundational components and the sophisticated implementations that integrate
them into intelligent systems. We first examine the foundational components:
context retrieval and generation, context processing and context management. We
then explore how these components are architecturally integrated to create
sophisticated system implementations: retrieval-augmented generation (RAG),
memory systems and tool-integrated reasoning, and multi-agent systems. Through
this systematic analysis of over 1300 research papers, our survey not only
establishes a technical roadmap for the field but also reveals a critical
research gap: a fundamental asymmetry exists between model capabilities. While
current models, augmented by advanced context engineering, demonstrate
remarkable proficiency in understanding complex contexts, they exhibit
pronounced limitations in generating equally sophisticated, long-form outputs.
Addressing this gap is a defining priority for future research. Ultimately,
this survey provides a unified framework for both researchers and engineers
advancing context-aware AI.