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Anthropic Releases Free Video Detailing Six Elements of Claude Prompt Structure

July 4, 2026 at 23:15 EDT

Foundation Models
Software Dev & Coding

Anthropic's Applied AI team has published a free workshop video of about 24 minutes explaining how to structure prompts for Claude, walking through the use of its "six elements" with hands-on examples. Titled "Prompting 101 | Code w/ Claude," the video is available on YouTube with no sign-up or paywall. Members of the Applied AI team use a real-world scenario to build up a prompt step by step.

Anthropic · Applied AI Team

The Six Elements of a Strong Claude Prompt

A free, no-signup workshop shows the six building blocks of an effective prompt — built and refined live on real tasks. The catch: most users lean on just one or two.

~24 min

Runtime, free on video — no registration, no paywall

Prompt elements, consolidated from an earlier 6–8 element structure

Techniques demonstrated live on real-world tasks

What most users use vs. what a full prompt uses

Beginners often settle for a vague one-liner — 1 element. A structured prompt stacks all 6.

typical
one-line prompt

fully structured
prompt

The Six Elements

1 · Role / task description

Clarify what to do and from what perspective.

2 · Dynamic context / data

Provide relevant context and data.

3 · Step-by-step instructions

Specify how to proceed, step by step.

4 · Examples (few-shot)

Show desired output patterns via examples.

5 · Output format

Specify the required format and structure.

6 · Constraints / reasoning cues

E.g. "think step-by-step," "say I don't know if unsure."

Positive reception

Seen as more valuable than paid courses
Pays off within the first few minutes
More consistent output; gains on agents & document generation

The caveats

Some material already familiar to docs readers
Watching alone does nothing without practice
XML tags & long-context ordering trip people up

The workshop aims to raise the baseline of a foundational skill — a stepping stone toward context engineering, which optimizes every token handled at inference time.

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