Context Engineering
Context engineering is the discipline of deliberately assembling everything an AI model sees at inference time — system prompt, retrieved documents, conversation memory, tool outputs, few-shot examples, and formatting scaffolding — so the model has exactly the information it needs to produce a high-quality response. Where prompt engineering focuses on the phrasing of a single user instruction, context engineering treats the entire input window as a designed artifact with layout, priority, and trade-offs. As context windows have grown and agentic workflows have become standard, what you put into the window, in what order, and what you leave out now drives output quality more than clever wording.
Example
A developer building a code review agent does not just write a better prompt — they decide which files to pull in via retrieval, how to summarize the diff, where to place the coding guidelines, how much of the issue tracker to include, and how to format tool outputs so subsequent turns stay coherent. Each of these is a context engineering decision.
Related Resources
Context Engineering: The 2026 Replacement for Prompt Engineering
How context engineering — the discipline of assembling what a model sees — replaced prompt engineering as the 2026 quality lever. Strategies, patterns, and trade-offs.
Context Engineering vs Prompt Engineering: The Difference Explained (2026)
Prompt engineering is about what you say. Context engineering is about what the model sees. Layered disciplines, different failure modes, and when each moves the needle.
Put this into practice
Build polished, copy-ready prompts in under 60 seconds with SurePrompts.
Try SurePrompts