Companion videos

The anatomy of a prompt: role, context, task, constraints, format — companion videos

The article breaks a good prompt into five named parts and explains why each one matters. These videos let you watch experienced practitioners assemble those parts on screen — first an Anthropic team building a real production prompt block by block, then a faster overview that shows how the same structure plays out across everyday tasks.

Primary pick

24:52

Prompting 101 | Code w/ Claude

Anthropic

Two members of Anthropic's Applied AI team build up a single prompt for a real customer use case (analyzing Swedish car accident reports), starting from a one-liner that fails and adding role, task context, dynamic content, examples, and final emphasis one layer at a time. It is the cleanest demonstration on YouTube of the exact "role + context + task + constraints + format" stack the article describes, with the failure modes shown along the way.

What you should get from this: Build stronger prompts by adding role, task context, examples, constraints and output format one layer at a time.

Watch or know first: Comfortable writing basic prompts; the article's five-part breakdown is the ideal warm-up.

AI Expert note: The demo uses Claude, but the layer-by-layer build maps one-to-one onto the article's role-context-task-constraints-format stack, whatever model you use.

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Also worth watching

20:17

Google's 9 Hour AI Prompt Engineering Course In 20 Minutes

Tina Huang

A condensed run through Google's prompt engineering course covering frameworks for clarity, iteration methods, and worked examples for emails, summaries, and analysis. Useful if you want a second pass that maps the same structural ideas onto the kinds of tasks you actually hit during a workday.

What you should get from this: You leave with a compact framework for prompt clarity and iteration, plus worked patterns for emails, summaries and analysis.

Watch or know first: None beyond the article — it works as a second pass over the same structural ideas.

AI Expert note: Watch it for breadth after the primary pick's depth; its value here is mapping the same structure onto everyday work tasks like emails, summaries and analysis.

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