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| Autori principali: | , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2507.13334 |
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| _version_ | 1866911067401093120 |
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| author | Mei, Lingrui Yao, Jiayu Ge, Yuyao Wang, Yiwei Bi, Baolong Cai, Yujun Liu, Jiazhi Li, Mingyu Li, Zhong-Zhi Zhang, Duzhen Zhou, Chenlin Mao, Jiayi Xia, Tianze Guo, Jiafeng Liu, Shenghua |
| author_facet | Mei, Lingrui Yao, Jiayu Ge, Yuyao Wang, Yiwei Bi, Baolong Cai, Yujun Liu, Jiazhi Li, Mingyu Li, Zhong-Zhi Zhang, Duzhen Zhou, Chenlin Mao, Jiayi Xia, Tianze Guo, Jiafeng Liu, Shenghua |
| contents | 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 1400 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_13334 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Survey of Context Engineering for Large Language Models Mei, Lingrui Yao, Jiayu Ge, Yuyao Wang, Yiwei Bi, Baolong Cai, Yujun Liu, Jiazhi Li, Mingyu Li, Zhong-Zhi Zhang, Duzhen Zhou, Chenlin Mao, Jiayi Xia, Tianze Guo, Jiafeng Liu, Shenghua Computation and Language 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 1400 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. |
| title | A Survey of Context Engineering for Large Language Models |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2507.13334 |