Comparison
Chain-of-Thought vs Few-Shot
Chain-of-Thought and Few-Shot are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for Chain-of-Thought
Chain-of-Thought comes up when the question is fundamentally about prompting.
"Solve this word problem step by step." — model shows working.
When you would reach for Few-Shot
When zero-shot quality is unstable or the output format is unusual.
Three (sentiment, label) pairs followed by a new sentence to classify.
Frequently asked
What is the difference between Chain-of-Thought and Few-Shot?
Chain-of-Thought: Chain-of-thought prompting asks the model to show its reasoning step by step before giving a final answer. It dramatically improves performance on multi-step problems. Few-Shot: Few-shot prompting includes a small number of input-output examples directly in the prompt so the model can pattern-match without any fine-tuning.
When should I use Chain-of-Thought vs Few-Shot?
Chain-of-Thought is the right concept when you are focused on prompting. When zero-shot quality is unstable or the output format is unusual.
Are Chain-of-Thought and Few-Shot the same thing?
No. Chain-of-Thought is prompting; Few-Shot is prompting. They are related but address different parts of the AI stack.