ReAct and CoT Prompting
Boosting LLM Reasoning with Step-by-Step Thinking
When you want better reasoning, decision-making, or multi-step answers from a language model, regular prompts often fall short.
That’s where two prompting strategies come in:
CoT (Chain-of-Thought) Prompting
ReAct (Reasoning + Acting)
These approaches help the model think step-by-step, just like a human solving a problem.
🔗 1. Chain-of-Thought (CoT) Prompting
Chain-of-Thought prompting guides the LLM to explain its reasoning step-by-step before giving a final answer.
Instead of jumping straight to an answer, the model is encouraged to "show its work."
🧪 Example:
Prompt:
“If Alice has 3 apples and gives 1 to Bob, how many does she have left?” Regular Answer: “2” CoT Prompted Answer: “Alice starts with 3 apples. She gives 1 to Bob. 3 - 1 = 2. So the answer is 2.”
✅ Great for:
Math word problems
Logical reasoning
Trivia with explanations
Scientific or legal arguments
🔁 2. ReAct Prompting (Reasoning + Acting)
ReAct goes beyond just thinking — it combines:
Reasoning steps (like CoT)
With Actions (like calling a tool or retrieving info)
ReAct lets the LLM plan its next step, take an action, and then continue reasoning.
🧪 Example Use Case:
“Who is the CEO of Tesla, and what’s their age?”
ReAct-style behavior:
Reason: “To answer this, I need to look up the current CEO.”
Action: [Search Tool → “CEO of Tesla” → "Elon Musk"]
Reason: “Now I need to find Elon Musk’s birth year.”
Action: [Search Tool → “Elon Musk age” → "52"]
Answer: “The CEO of Tesla is Elon Musk, who is 52 years old.”
✅ Great for:
Agentic workflows
Tool-using AI (search, calculator, database)
Dynamic decision trees
📊 ReAct vs CoT
Focus
Pure reasoning steps
Reasoning + interacting with tools
Use Cases
Math, logic, structured tasks
Agents, tool-using assistants
Output Style
"Step 1... Step 2..."
Thought → Action → Observation
Complexity
Simple to implement
Needs tools or memory integration
🧠 Summary
CoT: Helps models "think out loud" for more accurate answers
ReAct: Lets models reason and act — powering agent-like behavior
Both are essential for building smarter, explainable, interactive GenAI apps
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