A Survey of Context Engineering for Large Language Models

TL;DR

This survey comprehensively reviews the field of Context Engineering for Large Language Models, detailing components, implementations, and identifying key research gaps in system capabilities and output generation.

Contribution

It introduces a formal framework and taxonomy for Context Engineering, systematically analyzing over 1400 papers and highlighting critical research challenges and future directions.

Findings

Systematic taxonomy of Context Engineering components

Identification of a capability gap in long-form output generation

Analysis of over 1400 research papers in the field

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 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.