Chain-of-Thought Prompting
Chain‑of‑Thought (CoT) prompting encourages the model to break down a problem into intermediate reasoning steps before giving the final answer. This significantly improves performance on multi‑step reasoning tasks like math, logic, and common sense.
Chain‑of‑Thought prompting asks the model to "think step by step" to improve accuracy on complex problems.
Example: Without CoT
Prompt: "Roger has 5 tennis balls. He buys 2 cans of 3 balls each. How many does he have now?"
Model (without CoT) might guess incorrectly.
With Chain‑of‑Thought
Prompt: "Roger has 5 tennis balls. He buys 2 cans of 3 balls each. How many does he have now? Let's think step by step."
Model output:
"He starts with 5 balls. He buys 2 cans × 3 balls = 6 balls. Total = 5 + 6 = 11 balls."
Zero‑Shot CoT
Simply add "Let's think step by step" to any prompt. This often improves performance without providing examples.
Tree of Thoughts (ToT) – Advanced
Tree of Thoughts extends CoT by exploring multiple reasoning paths, evaluating each, and choosing the best. It’s more powerful but requires multiple API calls.
Two Minute Drill
- Chain‑of‑Thought prompts ask the model to show reasoning steps.
- Greatly improves math, logic, and multi‑step tasks.
- Zero‑shot CoT: simply add "Let's think step by step.".
- Tree of Thoughts explores multiple reasoning paths.
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