Context engineering frames prompt engineering within system level orchestration for large language model applications

Abstract By moving from isolated instruction crafting to system-level contextual orchestration, context engineering reframes how large language model (LLM) applications are designed for complex, multi-source workflows. Prompt engineering can deliver strong results for narrow, single-turn tasks, yet it often becomes brittle and costly to maintain as interaction length, tool use, governance constraints, and reliability requirements increase. In this perspective, context engineering is used as a working systems concept for the design and governance of context pipelines that determine how relevant data, tool outputs, memory cues, and policy constraints are selected, transformed, structured, validated, and delivered to the model. The term is positioned relative to adjacent paradigms rather than presented as a new model class. Retrieval-augmented generation is treated as a mechanism for context acquisition, tool-augmented reasoning as a mechanism for context creation via external actions, and memory services as a mechanism for context persistence. On this basis, the paper identifies recurring patterns in system design, including dynamic context assembly, token budgeting and compaction, multimodal context composition, memory management, provenance management, and observability. Issues related to anticipated modes of failure include context drift, provenance loss, privacy disclosure, and evaluation instability. The paper argues that system-level context control becomes increasingly important for LLMs when their applications are multi-turn, tool-using, policy-constrained, and provenance-sensitive.