Chain-of-Thought

Chain-of-Thought Prompting

What is Chain-of-Thought?

Chain-of-Thought (CoT) is a prompting technique that asks the model to explain its reasoning steps in detail before reaching the final answer.

Instead of "answer directly," you tell it: "think out loud, step by step."

Why Does CoT Work?

Neural networks produce tokens sequentially. When you ask the model to "think out loud," you're giving it extra computational space to develop reasoning before answering. This significantly improves accuracy on complex problems.

When to Use CoT?

✅ Good for:

• Math and logic problems
• Multi-step analysis
• Decisions weighing multiple factors
• Verifying reasoning chains

❌ Not necessary for:

• Simple, direct questions
• Creative tasks
• Basic translation

Ways to Activate CoT

Method 1: "Think step by step" The simplest approach. Just add to the end of your prompt: "Think step by step before giving your final answer"

Method 2: Few-Shot CoT Give an example of how to reason: "Here's an example of how to solve the problem: [example with thinking steps]. Now solve the following problem the same way."

Method 3: Explicit breakdown "Analyze the problem in steps:

1. Identify the given information
2. Analyze the relationships
3. Derive the solution
4. Verify the answer"

Comparison: With and Without CoT

Question: A store applies a 20% discount on a product worth $250, then adds 15% tax. What is the final price?

Without CoT (may err on complex versions): Rushes to an answer without showing work

With CoT: "Step 1: 20% discount = 250 × 0.2 = $50 Step 2: Price after discount = 250 - 50 = $200 Step 3: 15% tax = 200 × 0.15 = $30 Step 4: Final price = 200 + 30 = $230"

Practical Rule

The more complex and multi-step the task, the greater the value of Chain-of-Thought.