Context engineering: A repeatable AI workflow for product designers

"AI doesn't always "try harder" when you add detail. Usually, it's the opposite - it often gets lost. While handling complex reasoning tasks, the accuracy can go down by as much as 70%. But lots of designers still treat AI like a black box: dump in requirements, hope for magic, refine endlessly when it doesn't work."

"As designers, we understand that the way in which you structure information has a direct impact on how people process it. The same principle applies to AI - except AI is even more sensitive to structure than humans are."

"Context engineering is design thinking applied to AI - when you structure context like you'd design a system, AI becomes reliable and genuinely collaborative. When you don't, you're essentially asking someone to solve a puzzle while hiding half the pieces."

Adding excessive detail to AI prompts often decreases accuracy by up to 70% during complex reasoning tasks. Rather than treating AI as a black box where requirements are input and outputs refined endlessly, the solution lies in context engineering—applying design thinking principles to structure information for AI. Just as information structure impacts human processing, AI is even more sensitive to how context is organized. By systematically structuring context like designing a system, AI becomes reliable and collaborative. Without proper structure, AI lacks the information needed to think clearly, making the problem not about prompting techniques but about providing well-organized foundational information.

Read at Medium