Context Engineering: Why It Matters for a Business, Not Just Engineers

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Imagine hiring an exceptionally capable colleague who forgets everything overnight. Every morning starts with the same explanations: business terminology, approval workflows, and product logic. This is effectively how most companies work with AI today, repeatedly rebuilding context instead of reusing it. Context engineering offers a different approach by treating business knowledge as reusable infrastructure. In this article I will explore what context engineering is, where organizational context should live. Also, I will show how to test the approach on a real project in less than an hour. Context engineering is the discipline of preparing everything an AI model needs to understand a business before it receives a task. The focus is on building the knowledge foundation that makes those prompts meaningful — and building it once, in a form that any assistant can read. GitHub Copilot, Claude Code, and whatever the team adopts next all draw on the same foundation. This distinction is critical because most AI initiatives begin with prompt engineering. While prompts define the request, they do not provide the organizational knowledge required to answer it correctly. They cannot capture:

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