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How to ThinkIn the Age of AI

Context Engineering

The practice of designing structured information environments that enable AI agents to reason effectively. Unlike prompt engineering, which optimizes individual instructions, context engineering focuses on the surrounding architecture — what knowledge is available, how it connects, and what constraints guide decision-making. It shifts the focus from "what to say" to "what to know."

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Context Engineering Principles: The Mental Models I Use Every Day with AI Agents

Named principles for context engineering — less is more, knowledge gardening, structure over retrieval — discovered through daily agent usage, not academic theory.

Context Engineering in Practice: How the Shift from Prompting Actually Happens

The practitioner transformation from prompt-first to context-first AI work — with real before/after artifacts, four progressive stages, and a discrimination test for knowing when each approach is right.

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Context Engineering is taught in Context Engineering for Humans — a guided sequence of 16 lessons.

After completing this path you will engineer context deliberately: recognize which context you are in and load it on purpose, externalize the assumptions and decisions that machines and colleagues silently need, structure information so it survives handoff (identifiers, atomic units, explicit relationships), and manage the switching costs that fragment thinking. Your AI sessions start warm and your writing stops being misread.

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