Summary: Context architecture applies information architecture principles to AI systems, helping agents interpret information and produce better, user aligned responses.
From Prompts to Context
The way we shape AI products has evolved quickly.
It started with prompt engineering. Early on, success depended on crafting the right instruction. A well-written prompt could unlock surprisingly strong results, and teams began collecting and reusing prompts as assets.
Then came context engineering, a concept coined by Tobi Lutke . Teams realized that prompts alone were not enough; what matters is a concise collection of context-specific guidance, targeted to the task at hand: instructions, retrieved knowledge, narrow tools, selected memory, and state all working together. The task shifted from writing prompts to orchestrating context.
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