Context Engineering in plain English.

Context engineering is the practice of deliberately curating everything an AI model sees at a given moment — not just the instruction you write, but which documents get retrieved, which past messages are kept or summarized, which tool results are included, and in what order — to fit within the model's context window while maximizing the chance it has what it needs. As AI agents took on longer, multi-step tasks through 2025-2026, teams found that clever prompting alone stopped being the bottleneck; the harder problem became managing an ever-growing pile of conversation history, tool outputs, and retrieved documents without drowning the model in irrelevant information or blowing past its context limit and budget. Techniques include summarizing older turns, retrieving only the most relevant document chunks instead of whole files, and explicitly pruning tool output before it re-enters the model's context. Context engineering is increasingly described as the more important skill of the two, with prompt engineering as just one piece of it.