Context Engineering

Prompt engineering focuses on wording a single request. Context engineering takes a wider view. The model receives one sequence of tokens, and everything in it competes for space and for the model's attention. That sequence usually includes a system prompt, tool definitions, retrieved passages, prior turns, tool outputs and sometimes long-term memory. The term became common in 2025, when teams building agents found that many failures came from the model having the wrong information, too much of it or conflicting information, rather than from poor phrasing. Several principles follow. First, relevance beats volume. Some models accept hundreds of thousands of tokens, but they do not use every token equally well. The 'lost in the middle' study (Liu et al., 2023) found that models often use information at the start and end of a long input better than information buried in the middle. Second, context is a budget. Every token costs money and adds latency, and irrelevant material can distract the model. Third, structure helps. Clear sections, labeled documents and consistent formats make it easier for the model to find what matters. Common techniques include: - retrieval-augmented generation, which fetches only the relevant passages - just-in-time loading, where an agent holds lightweight references such as file names or IDs and uses tools to pull full content when needed - structured note-taking that keeps state outside the window - compaction of long histories - sub-agents that explore in their own context and return condensed findings One misconception is that context engineering is just a new name for prompting. Wording is one part of it, but most of the work is systems design: deciding what gets retrieved, summarized or dropped, and in what order it appears. Another misconception is that a bigger window removes the need for this. A bigger window lowers the pressure, but cost and attention limits remain.