Context Engineering

Context engineering is the practice of deliberately designing and assembling all information supplied to a language model's context window — including instructions, retrieved documents, tool outputs, memory summaries, and conversation history — to maximize model performance on a specific task.

Context engineering treats everything passed into a language model's context window as a design artifact requiring deliberate construction and optimization. While prompt engineering focuses primarily on the instruction text itself, context engineering encompasses the full information environment the model receives: system instructions, conversation history, retrieved knowledge via retrieval-augmented generation (RAG), structured data, tool-call outputs, code, uploaded documents, and external memory summaries. The term gained significant traction in 2025 as AI practitioners recognized that ad-hoc or naive context assembly was a primary driver of suboptimal performance and inconsistent behavior in production AI systems, independent of model capability.

Context engineering involves several interconnected design decisions. Selection determines which information is relevant enough to include — even models with million-token context windows perform better when context is focused, because irrelevant content dilutes attention and increases noise. Ordering matters concretely: empirical research published in 2023 demonstrated that language models retrieve information less reliably when it is placed in the middle of long contexts compared to the beginning or end, a finding labeled the "lost in the middle" problem, making the positioning of critical content an explicit design variable. Compression decisions — when and how to summarize prior conversation turns or lengthy documents — balance recall against token costs. For agentic multi-step systems, context engineers must additionally manage tool-output formatting, cross-step state representation, and session-to-session memory architectures to keep the model oriented without flooding the window with irrelevant intermediate content.

Context engineering matters because the same underlying model can produce dramatically different results depending on how its context is assembled. A model answering a technical support query that receives the relevant product documentation section, the customer's account history, and a concise task instruction will outperform the same model given an unfocused context containing the entire product catalog alongside unrelated prior conversations. As AI applications grow more complex — incorporating multiple retrieved documents, multi-agent coordination, external tool outputs, and long interaction histories — systematic context management becomes as important a performance lever as model selection or prompt phrasing.

As of 2026, context engineering is recognized as a distinct layer of AI application architecture with dedicated tooling. Frameworks such as LangChain, LlamaIndex, and the OpenAI Assistants API provide abstractions for retrieval, memory, and dynamic context assembly. Production systems commonly implement semantic chunking for document retrieval, context distillation (converting long interaction histories into compact memory summaries), and dynamic few-shot selection (retrieving the most relevant examples at inference time rather than hardcoding them). The discipline is especially central to agentic AI systems, where chains of tool calls generate large volumes of intermediate content that must be filtered and restructured to maintain model focus across tasks spanning many steps.