Скачать или смотреть Chain-of-Thought Prompting Explained (Make AI Reason Better)

Ever noticed AI confidently giving wrong answers?
Chain-of-thought prompting fixes this by forcing step-by-step reasoning.
This video explains what it is, why it works, and how to use it properly.


Chain-of-thought prompting is one of the most powerful techniques for getting
more accurate and reliable answers from large language models.

In this video, we break down:

• What chain-of-thought prompting is
• Why it works on a deep level (reasoning, decomposition, emergent abilities)
• Few-shot vs zero-shot chain-of-thought
• Simple prompts that massively improve AI reasoning
• Advanced techniques like self-consistency and least-to-most prompting
• When chain-of-thought helps — and when it doesn’t with newer models

You’ll also learn why some modern reasoning-optimized models already
have step-by-step logic built in, and how that changes prompting strategy.

This video is perfect for:
• AI learners and engineers
• Prompt engineers
• Anyone using LLMs for reasoning, math, planning, or logic

Chain-of-thought remains one of the most useful tools in the AI prompting toolkit.


00:00 Why AI Gives Confidently Wrong Answers
00:48 What Is Chain-of-Thought Prompting?
01:15 Showing the Work: The Core Idea
01:32 Why Chain-of-Thought Works
02:04 The Power of Decomposition
02:28 Learning How to Think (Not Just Answers)
02:56 Few-Shot Chain-of-Thought
03:14 Zero-Shot Chain-of-Thought
03:55 Auto Chain-of-Thought
04:06 When Simple Prompts Aren’t Enough
04:12 Self-Consistency Technique
04:56 Least-to-Most Prompting
05:20 Reality Check with Modern AI Models
06:05 When Chain-of-Thought Is Still a Superpower
06:36 The Future of Prompting