# A Survey of Context Engineering for Large Language Models

**Authors:** Lingrui Mei, Jiayu Yao, Yuyao Ge, Yiwei Wang, Baolong Bi, Yujun Cai, Jiazhi Liu, Mingyu Li, Zhong-Zhi Li, Duzhen Zhang, Chenlin Zhou, Jiayi Mao, Tianze Xia, Jiafeng Guo, Shenghua Liu  
**arXiv:** [2507.13334](https://arxiv.org/abs/2507.13334)  
**Date:** 2025-07-17  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2507.13334v1/

## Key points

- Contextual information plays a crucial role in determining the performance of Large Language Models (LLMs) during inference.
- The study introduces Context Engineering as a formal discipline that optimizes information payloads for LLMs beyond prompt design.
- Foundational components of Context Engineering include context retrieval and generation, context processing, and context management.
- Advanced system implementations like retrieval-augmented generation (RAG), memory systems with tool-integrated reasoning, and multi-agent systems are created by integrating these foundational components.
- An analysis of over 1300 research papers reveals an imbalance in model capabilities: while current models excel at grasping complex contexts, they struggle with generating sophisticated long-form outputs.
- Addressing this gap is identified as a key priority for future research endeavors in the field of LLMs and Context Engineering.

## Summary

The survey titled "A Survey of Context Engineering for Large Language Models" by Lingrui Mei et al. explores the crucial role of contextual information in determining the performance of Large Language Models (LLMs) during inference. The study introduces Context Engineering as a formal discipline that goes beyond prompt design to systematically optimize information payloads for LLMs. By breaking down Context Engineering into its foundational components and exploring sophisticated implementations, the authors provide a comprehensive taxonomy for understanding this field. The foundational components examined include context retrieval and generation, context processing, and context management. These elements are then architecturally integrated to create advanced system implementations such as retrieval-augmented generation (RAG), memory systems with tool-integrated reasoning, and multi-agent systems. Through an extensive analysis of over 1300 research papers, the survey establishes a technical roadmap for the domain and highlights a critical research gap: an inherent imbalance in model capabilities. While current models enhanced by advanced context engineering excel in grasping complex contexts, they struggle with generating equally sophisticated long-form outputs. The authors emphasize addressing this gap as a key priority for future research endeavors. Ultimately, this survey offers a unified framework beneficial for both researchers and engineers working on advancing context-aware AI technologies. With ongoing work and significant citations backing their findings, Lingrui Mei et al. 's survey stands as a substantial contribution to the field of LLMs and Context Engineering.