Abstract

The performance of Large Language Models (LLMs) is fundamentally determinedby the contextual information provided during inference. This survey introducesContext Engineering, a formal discipline that transcends simple prompt designto encompass the systematic optimization of information payloads for LLMs. Wepresent a comprehensive taxonomy decomposing Context Engineering into itsfoundational components and the sophisticated implementations that integratethem into intelligent systems. We first examine the foundational components:context retrieval and generation, context processing and context management. Wethen explore how these components are architecturally integrated to createsophisticated system implementations: retrieval-augmented generation (RAG),memory systems and tool-integrated reasoning, and multi-agent systems. Throughthis systematic analysis of over 1300 research papers, our survey not onlyestablishes a technical roadmap for the field but also reveals a criticalresearch gap: a fundamental asymmetry exists between model capabilities. Whilecurrent models, augmented by advanced context engineering, demonstrateremarkable proficiency in understanding complex contexts, they exhibitpronounced 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 engineersadvancing context-aware AI.