Context Engineering for Large Language Models
Context engineering is the discipline of curating and maintaining the optimal set of tokens during large language model (LLM) inference, shifting the focus from "how to word a prompt" to "what information environment an AI system needs to succeed"[^c1]. Unlike prompt engineering, which centers on writing effective instructions, context engineering treats the entire context window as a design surface and intentionally constructs the information supplied to the model at each step[^c2]. In production AI systems that operate across multiple turns, the initial prompt constitutes only 5 to 10 percent of the context window; the remaining 90 percent — conversation history, retrieved documents, tool results, and structured state — determines whether the system succeeds or fails[^c3].
The transformer architecture, which enables every token to attend to every other token across a sequence, creates a quadratic number of pairwise relationships. As context length increases, this attention budget is stretched thin, and model accuracy degrades — a phenomenon known as context rot[^c1]. Effective context engineering therefore aims to find the smallest possible set of high-signal tokens that maximize the likelihood of a desired outcome. Thoughtworks' Technology Radar classified context engineering as a core architectural concern in April 2026, recommending progressive context disclosure, prompt caching, dynamic retrieval, and stateful compression as key practices[^c2].
Modern context windows range from 2,048 tokens in early models like GPT-2 to 2 million tokens in Gemini 1.5 Pro, yet larger windows do not always improve performance[^c4]. Information placed in the middle of long contexts is less reliably processed than information at the beginning or end, and accuracy can drop sharply when context is unfocused. A focused 300-token context often outperforms an unfocused 113,000-token context[^c3]. Core strategies for managing limited context budgets include external memory systems, aggressive compression and summarization, selective retrieval, and context isolation through multi-agent architectures[^c3].