How to Break Down Complex Tasks for AI

Описание: Why does AI give you disappointing results even when you're specific about what you need?
In this video, I break down one of the most practical AI skills you can develop: task decomposition. The problem isn't AI's capability. It's that most people ask it to juggle multiple objectives simultaneously, which splits its focus and dilutes quality across the board.
I'll show you exactly how to recognize when a task needs to be broken down, walk you through a simple 4-step framework for decomposition, and demonstrate real examples of how this transforms generic outputs into genuinely useful results.
What You'll Learn:
• The core principle that transforms AI results from mediocre to exceptional
• How to recognize sequential vs. parallel task complexity
• The 4-step decomposition framework you can apply immediately
• Real example: turning a vague hiring process request into focused, high-quality outputs
• When to break tasks down vs. when one prompt is perfectly fine
• How this connects to context windows and why smaller tasks = better quality
• The most common mistake people make when learning decomposition
Resources:
• Eau Claire AI: www.eauclaireai.com
• Need help applying this to your business? Let's talk: www.eauclaireai.com
Previous Videos Referenced:
• Video 2: Why AI Gets Confused (context windows)
• Video 12: Chain of Thought prompting
• Video 5: What's Actually Happening When You Change How You Prompt
• Video 4: Why AI Is Good at Some Things and Terrible at Others
This is part of the Making AI Make Sense series, where we build genuine conceptual understanding of how AI works. Not just tips and tricks, but the foundational knowledge that lets you reason independently about AI.
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