Prompting Techniques: Zero-shot, Few-shot, and Chain-of-Thought (CoT)

Описание: Are you tired of asking AI complex questions only to get "meh" generic answers? The problem usually isn’t the model—it’s the prompt.

In this deep dive, we unpack the science of In-Context Learning. We move beyond simple questions to master advanced techniques like Few-Shot Prompting, Chain of Thought (CoT), and Self-Consistency. Learn how to force the AI to "show its work," drastically reduce errors, and unlock strategic reasoning capabilities you didn't know it had.

Whether you are a beginner or looking to refine your engineering skills, this playbook will help you bridge the gap between a basic chatbot and a reasoning engine.

📋 What You’ll Learn:
Why Direct Questions Fail: Understanding the "Black Box" problem.

Shot Prompting: The difference between Zero-Shot, One-Shot, and Few-Shot prompting.

Chain of Thought (CoT): How to make the AI break down problems step-by-step.

Advanced Tactics: Using "Self-Consistency" to fact-check the AI against itself.

The Playbook: Exactly when to use these techniques (and when not to).

⏱️ Timestamps:
00:00 - Intro: The difference between a "meh" answer and a game-changer. 01:09 - Why Your Prompts Fail: The limits of direct questions. 01:31 - Technique 1: Shot Prompting (Giving Examples). 02:02 - Zero-Shot vs. One-Shot vs. Few-Shot explained. 02:45 - Technique 2: Chain of Thought (Unlocking Reasoning). 03:17 - Opening the "Black Box": Asking AI to show its work. 03:54 - Technique 3: Advanced CoT ("Let's think step-by-step"). 04:38 - Technique 4: Self-Consistency (The majority vote method). 05:16 - The Prompting Playbook: When to use CoT vs. Standard prompting. 05:58 - Top 3 Prompting Mistakes to Avoid. 06:22 - Key Takeaway: The "How" is as important as the "What."

🧠 Key Concepts Explained:
In-Context Learning (ICL): Teaching the model a pattern within the prompt itself without retraining it.

The Magic Phrase: Simply adding "Let's think step-by-step" changes how the model processes information, forcing it to generate intermediate reasoning before the final answer.

Self-Consistency: Asking the AI for multiple reasoning paths and picking the answer that appears most frequently (a "vote" for accuracy).

🚫 3 Mistakes to Avoid:
Being Vague: Don't just say "think carefully"—tell it what factors to consider.

Overwhelming the Model: Keep reasoning tasks to 5–7 steps for the sweet spot.

Ignoring the Reasoning: Don't just grab the final answer; the logic path is often where the real value lies.

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