Comparison
Chain-of-Thought vs ReAct
Chain-of-Thought and ReAct 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 ReAct
ReAct comes up when the question is fundamentally about agents & tools.
A search-augmented assistant: Thought, Search, Observation, Thought, Answer.
Frequently asked
What is the difference between Chain-of-Thought and ReAct?
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. ReAct: ReAct is a prompting pattern that interleaves reasoning ("Thought:") with actions ("Action:") and observations ("Observation:"). It is the foundation of most tool-using agents.
When should I use Chain-of-Thought vs ReAct?
Chain-of-Thought is the right concept when you are focused on prompting. ReAct applies when you are focused on agents & tools.
Are Chain-of-Thought and ReAct the same thing?
No. Chain-of-Thought is prompting; ReAct is agents & tools. They are related but address different parts of the AI stack.