Context engineering as the real skill
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The gap between mediocre and remarkable AI output is almost always context. Paul and Daniel dig into context engineering: what to include, what to leave out, and how to build reusable context that pays off every session.
Key ideas
Show notes
If prompting is the visible skill, context engineering is the one that actually moves quality. Paul and Daniel go deep on how to assemble the right context for a task, why more isn't always better, and how to build context assets you reuse instead of rebuilding every time.
Timestamps
Quotable
“Think of context as an asset. Build it once, and it pays off every session after.”
— Paul Maxwell
Full transcript
Paul: The difference between mediocre and remarkable output is almost never the wording of the prompt. It's the context. What does the model actually know about your situation before it starts?
Daniel: And more isn't automatically better. Dump everything and you drown the signal. The skill is choosing what matters for this task and leaving the rest out.
Paul: Think of context as an asset. You build a good pack once, the schema, the conventions, the examples, and it pays off every single session after that.
Daniel: Too thin and the model guesses. Too noisy and it gets distracted. Context engineering is finding that middle, and it's a real, learnable skill.
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