AI Vocabulary · 03 Working with AI
What is Context Engineering?
The craft of choosing what goes into the context, and what stays out, so the model can do good work.
Context engineering goes beyond writing a good prompt. It is about designing the whole information supply of an AI system: which documents to retrieve, which instructions to load, what history to keep, what to summarise and what to leave out.
It matters most for agents and longer workflows, where the context is assembled automatically over many steps. Too little and the model guesses. Too much and it gets distracted, slow and expensive.
Why it matters for you
When an AI pilot works in a demo but fails with real users, the problem is often context: the right information isn't there at the right moment. Fixing that is usually cheaper than a bigger model.
Own it
Well-structured knowledge, such as clear documents, decision logs and examples, is the raw material of context engineering. Investing in it pays off with every model you will ever use.
Where it comes from
The term took off in June 2025. Shopify CEO Tobi Lütke said he preferred it to "prompt engineering", and Andrej Karpathy backed it, describing the work as filling the context window with just the right information for the next step. Simon Willison documented the shift on his blog, noting the term had only recently started gaining traction.
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