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Loop Engineering for Beginners Stop Prompting, Start Design
Published 8/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 3h 24m | Size: 1.41 GB
A practical method for AI workflows that finish themselves, verify themselves, and know when to stop -- with human appro
What you'll learn
Hand off a goal to an AI using the three-part goal packet, instead of writing a step-by-step instruction sheet
Write a done-condition that's true or false just by looking at the result -- no ambiguity
Design a loop where the AI verifies its own work before reporting back
Build a loop that runs on a schedule and has draft work waiting for you every morning
Design failure handling -- retries, notifications, and logs -- for a loop that runs unattended
Build the same kind of loop in a no-code tool, and know when to use which approach
Design a human approval gate so nothing risky (publishing, sending, deleting) happens without a person's sign-off
Diagnose a loop that isn't working, and decide whether to fix it or kill it
Requirements
A Mac is required. Every screen in this course is the macOS desktop app.
A Claude account is required.
No programming experience is required -- every technique in this course is plain-English requests or a visual, box-and-arrow no-code tool.
Prior comfort with an AI chat or agent tool is recommended (this course reviews the basics but isn't a from-zero introduction).
Description
This course contains the use of artificial intelligence.
"I don't really do prompt engineering anymore. There's a loop, and the loop is prompting Claude and deciding what to do." -- Boris Cherny, Claude Code's creator at Anthropic.
AI tool builders started saying things like this in 2026.
Instead of typing an instruction every single time, they design a system that runs on its own until the goal is actually done.
This way of working is called loop engineering, and it's spreading fast.
This course teaches that design method, hands-on.
It doesn't stop at explaining the concept -- by the end, you'll have one real loop from your own work running automatically, every day.
The first half teaches six design components
- How to hand off a goal instead of writing a step-by-step instruction sheet
- How to write a done-condition that's true or false just by looking at the result
- Self-verification -- having the AI grade its own output before it reports back
- Stop conditions, so a loop doesn't run forever on a bad path
- Audit reporting -- two sentences that remove the ability to fake progress
- Human approval gates, so nothing risky ships without a person clicking "go"
These six ideas don't go stale when the buzzword changes -- they're the load-bearing structure underneath whatever the industry calls this next.
The second half is hands-on.
You'll run one full loop in front of the camera with Claude Code -- goal handoff to audit report, one full cycle. Then you'll turn that into something that runs automatically every morning. You'll see the same idea built in a no-code tool, and learn when to reach for which. And in the most important lecture in the course, you'll build the safety design that keeps a human in the loop before anything gets published or sent.
What this course does not cover
- Programming -- everything is plain-English requests and visual, box-and-arrow tools
- A specific no-code vendor's exact UI (the workflow shape is taught, not one tool's pixel-for-pixel screen)
- Setting up a Claude account or an AI chat tool from zero (see the prerequisite course for that)
Who this course is for
Anyone who uses ChatGPT or Claude for work but still types a fresh instruction every time
Anyone who understands the AI-agent concept but hasn't built something that keeps running without supervision
Owner-operators and managers who want to safely hand off routine work to AI
Anyone who's taken an AI-agent course and is looking for the next concrete step
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