A Survey of Context Engineering for Large Language Models
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.
6879aad021b37e676c8e406b
Lingrui Mei
claimed_verified
2025-07-18T07:49:58.423Z
63120517ae8896941da4c5da
/avatars/10e1be026035f3e24225e6782a710083.svg
Lingrui Mei
user
Chevalier
6879aad021b37e676c8e406c
Jiayu Yao
claimed_verified
2025-07-18T07:49:48.326Z
671f9cd9ff056a1b49444f37
https://cdn-avatars.huggingface.co/v1/production/uploads/671f9cd9ff056a1b49444f37/ae-2cqWrKMG3RfFBKfZHj.jpeg
duoduo yao
user
Theodyy
6879aad021b37e676c8e406d
Yuyao Ge
claimed_verified
2025-07-18T07:49:54.689Z
656ad93853703dd78f3de7b8
https://cdn-avatars.huggingface.co/v1/production/uploads/656ad93853703dd78f3de7b8/r6VB6ICND1td3wFOy8pnz.jpeg
YuyaoGe
user
YuyaoGe
6879aad021b37e676c8e406e
Yiwei Wang
6879aad021b37e676c8e406f
Baolong Bi
claimed_verified
2025-07-29T07:10:47.634Z
642577e06d0f0f5f1dc68904
/avatars/3df7cfcf9ca6cd9c9e9b10a73f4efc35.svg
Bibaolong
user
Bibaolong
6879aad021b37e676c8e4070
Yujun Cai
6879aad021b37e676c8e4071
Jiazhi Liu
claimed_verified
2025-07-18T12:31:49.586Z
687382c6e5468c287165b2dc
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/N9nIkaHWkeML8PgbQHzHy.png
Liujz
user
juhyun13
6879aad021b37e676c8e4072
Mingyu Li
claimed_verified
2025-07-18T07:49:56.619Z
6720cf97a0396f933ec93ab8
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/NAdPIISe9-TYGGM0nAgsb.png
Li Max
user
LImax72
6879aad021b37e676c8e4073
Zhong-Zhi Li
claimed_verified
2025-07-22T07:53:58.184Z
6545f575650796837446afca
/avatars/df9fab381e5d43af753b8fc2c7d35865.svg
Zhongzhi Li
user
lizhongzhi2022
6879aad021b37e676c8e4074
Duzhen Zhang
claimed_verified
2025-07-18T07:49:50.488Z
662383a20edeabfe3b64a6a5
/avatars/a76da726002d853dd08a51a6af6311d9.svg
Duzhen Zhang
user
ShowerMaker
6879aad021b37e676c8e4075
Chenlin Zhou
claimed_verified
2025-07-18T07:49:52.410Z
669e27902dbf53ccd23ae47f
/avatars/5c193b542dcfe2e64467fa5c686f3e20.svg
chenlin
user
tvstfe
6879aad021b37e676c8e4076
Jiayi Mao
claimed_verified
2025-07-21T08:30:09.783Z
661f6cd6192707c19db98a92
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/-9x0_KXz1nKvCPtE2MtPf.png
Jiayi Mao
user
MJy1023
6879aad021b37e676c8e4077
Tianze Xia
6879aad021b37e676c8e4078
Jiafeng Guo
6879aad021b37e676c8e4079
Shenghua Liu
A Survey of Context Engineering for Large Language Models
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.
Context Engineering systematically optimizes information payloads for Large Language Models, addressing gaps in generating sophisticated, long-form outputs.
Large Language Models Context Engineering context retrieval context generation context processing context management retrieval-augmented generation memory systems tool-integrated reasoning multi-agent systems