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
Published: 2025-07-17 17:50:36

Abstract: 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.