Chain-of-thought prompting means asking a large language model to show its reasoning step by step instead of giving the final answer directly.
It’s simply:
➡️ “Think step by step.”
➡️ “Explain your reasoning.”
➡️ “Show the intermediate steps.”
This technique helps the model produce more accurate, more logical, and more transparent answers — especially for math, coding, planning, or multi-step problems.
🔍 How it works
You add a prompt like:
- “Explain your reasoning step by step.”
- “Let’s reason it out logically.”
- “Show your chain of thought.”
The model then writes something like:
- I identify the variables
- I compute X
- I check condition Y
- Therefore, the result is Z
This is the chain of thought — the model’s internal reasoning written out explicitly.
📌 Why it’s useful
Because it helps the model:
- avoid logical mistakes
- break a complex task into small steps
- explain the logic behind the answer
- be more reliable for math and reasoning
- plan actions clearly (agents, workflows, RAG pipelines, etc.)
As an AI Engineer, you’ll use this technique often when building:
- agents
- reasoning pipelines
- RAG systems needing proper justification
- evaluation workflows
- chain‐based frameworks (LangChain, LlamaIndex…)
👀 Example (simple)
Question:
If a car drives 60 km/h for 90 minutes, how far does it go?
Chain-of-thought prompting:
“Explain step by step.”
Model output:
- 90 minutes = 1.5 hours
- Distance = speed × time = 60 × 1.5 = 90 km
➡️ Final answer: 90 km
🧠 In short
Chain of thought prompting = ask the model to think step by step.
It’s one of the most important techniques in modern prompt engineering — especially when you’re building LLM apps that require reasoning.