What is chain-of-thought prompting?

It is a technique where a language model is prompted to generate intermediate reasoning steps before its final answer, which typically improves accuracy on complex tasks.

Why does chain of thought improve accuracy?

It gives the model more structured space to work through a problem incrementally, similar to how a person shows their work, reducing errors from jumping straight to a conclusion.

How do I use chain-of-thought prompting?

You can explicitly ask the model to 'think step by step' or 'explain your reasoning', or provide few-shot examples that demonstrate worked-out reasoning chains.

Is chain of thought the same as a reasoning model?

Not exactly. Chain of thought is a prompting technique, while some newer models generate extended internal reasoning by default as part of their architecture and training.

Does chain of thought slow down responses?

Yes, generating intermediate reasoning steps requires more tokens, which increases latency and cost, but often improves reliability for complex tasks.

Is chain of thought useful for simple tasks?

It generally adds the most value for multi-step reasoning, math, and logic problems, and offers little benefit for simple factual lookups.

AI Fundamentals

Chain of Thought

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Chain of thought is a prompting technique that encourages a large language model to generate intermediate reasoning steps before producing a final answer, improving performance on complex reasoning tasks.

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Definition

Chain of thought is a prompting technique that encourages a large language model to generate intermediate reasoning steps before producing a final answer, improving performance on complex reasoning tasks.

Overview

Rather than asking a model to jump straight to an answer, chain-of-thought prompting asks it to "think step by step," breaking a problem into smaller intermediate steps the way a person might work through a math problem on paper. This has been shown to meaningfully improve accuracy on arithmetic, logic, and multi-step reasoning tasks compared to asking for a direct answer, because it gives the model more tokens and structure to work through before committing to a conclusion. Chain of thought can be elicited explicitly through prompt engineering — for example, instructing the model to "explain your reasoning" — or it can be built into a model's default behavior, as seen in reasoning-focused models that generate extended internal reasoning traces before responding. It is a form of in-context reasoning that complements, rather than replaces, techniques like few-shot learning, where example reasoning chains are shown to the model in the prompt. The technique is widely used in production systems for tasks like code generation, mathematical problem solving, and complex decision-making pipelines, including AI agents that need to plan multi-step actions. The course AI Agents & Agentic Workflows and the blog post Prompt Engineering: Get Better Results from Any LLM both cover practical techniques for using it effectively.

Key Concepts

Elicits intermediate reasoning steps before a final answer
Improves accuracy on arithmetic, logic, and multi-step reasoning tasks
Can be prompted explicitly or built into a model's default reasoning behavior
Often combined with few-shot examples showing worked reasoning chains
Trades additional generated tokens for improved answer quality
Widely used in agentic and multi-step planning workflows

Use Cases

Mathematical and logical problem solving

Complex code generation and debugging

Multi-step planning in AI agents

Structured decision-making and analysis pipelines

Improving reliability of LLM-generated answers in high-stakes tasks

Explaining and auditing a model's reasoning process

Frequently Asked Questions

From the Blog

AI & Technology

Prompt Engineering: Get Better Results from Any LLM

The difference between a mediocre AI output and an excellent one is usually the prompt. This guide covers the techniques that consistently produce better results: clarity, context, examples, chain-of-thought, system prompts, and output formatting — with real before/after examples.