Context engineering is deciding what the model should read and how it is arranged before it replies, so outputs are grounded, auditable, and less prone to guesswork.
Prompt and context
Prompt engineering is the wording of your instruction to a generative AI model. Context engineering is the broader practice of deciding what the model should read, what instructions govern the task, and how that material is arranged before it replies. Put simply, prompts steer the style and task, while context provides the material and guidance to reason upon.
Better inputs
Strong results come from careful curation. Select a small set of reliable documents, keep them up to date, split them into named sections with a single-sentence summary, and use a simple output schema so the AI maps facts to fields rather than guessing.
Why context matters
It matters now because generous context limits encourage copy-and-paste, which can increase cost and confusion. Tighter, well-labelled inputs produce clearer answers, easier audits and fewer surprises, which is what stakeholders expect from AI.