What is chain of thought (CoT) prompting?

What is chain of thought prompting?

Chain of thought (CoT) prompting is a technique that encourages AI models to generate their reasoning process step by step, rather than jumping straight to an answer. By asking the model to “show its work,” users often get more accurate, transparent, and trustworthy results—especially on tasks involving logic, math, or multi-step reasoning.

This technique is increasingly useful in business, research, and everyday decision-making, where understanding how an answer was derived is just as important as the answer itself.

Where did chain of thought prompting come from?

Chain of thought prompting gained visibility in 2022 after researchers from Google Brain, including Jason Wei and Denny Zhou, released a foundational paper titled “Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.”

The researchers showed that explicit reasoning instructions in prompts significantly improved performance on tasks like arithmetic word problems and logical inference—but only when applied to sufficiently large models (like PaLM and GPT-3+ scale).

Their findings highlighted a key insight: the ability to reason improves when language models are guided to articulate intermediate steps, much like a student solving a math problem out loud. This sparked wide adoption across AI research, developer communities, and enterprise use cases.

Originally explored in mathematical contexts, chain of thought prompting is now used across:

• Data analysis and forecasting
• Business strategy evaluation
• Scientific hypothesis generation
• Coding, troubleshooting, and logic puzzles
• Educational and tutoring scenarios

How does chain of thought prompting work, and how is it typically used today?

Chain of thought prompting works by including instructions in your prompt that signal the model to think through the problem slowly and methodically. Common cue phrases include:

• “Let’s think step by step”
• “Break this down logically”
• “Explain your reasoning before giving a final answer”

You can use this technique in zero-shot, few-shot, or even multi-shot prompting formats:

• Zero-shot: Just add a phrase like “Think step by step” to the end of your question
• Few-shot: Provide example Q&A pairs that model the reasoning process you want it to follow
• Chained prompting: Ask for reasoning first, then follow up with targeted questions to validate or expand the logic

Chain of thought prompting tends to work best with large, reasoning-capable models like ChatGPT (GPT-4), Claude, Gemini (formerly Bard), or open-source models fine-tuned for reasoning tasks. Older or smaller models often struggle to produce coherent stepwise logic.

For example, a business analyst might write:

“Analyze our Q2 sales data and identify which region underperformed. Think step by step: first consider historical seasonality, then compare YOY growth, and finally evaluate pricing changes or macroeconomic factors.”

Instead of receiving a vague answer, the AI provides a structured walkthrough. This lets the analyst verify each step, flag any incorrect assumptions, and build trust in the output.

These variations of the term "prompt engineering" have also led to similar phrases like context engineering and more.

Why does chain of thought prompting matter?

Chain of thought prompting isn’t just a better way to interact with AI—it’s a more rigorous approach to problem-solving.

Here’s why it matters for teams:

• It reduces hallucinations by forcing models to build answers from intermediate reasoning, not guesses
• It increases transparency, helping users see where logic holds or breaks down
• It improves decision confidence, especially when outputs inform real-world actions

For organizations embedding AI into workflows, chain of thought prompting becomes a guardrail. Instead of blindly trusting black-box outputs, teams can trace reasoning and make adjustments as needed.

It also improves human thinking. Encouraging stepwise explanations builds internal habits of clarity and logic—whether you’re a marketer reviewing campaign metrics or a PM evaluating trade-offs.

In practice:

• Marketing teams: When analyzing campaign performance, they can ask AI to evaluate results channel by channel—starting with audience targeting, then cost efficiency, followed by conversion rates—before recommending budget reallocations
• Product teams: When making prioritization decisions, they can have AI weigh customer feedback, engineering effort, business impact, and strategic fit—step by step—to surface trade-offs and justify roadmapping choices
• Sales teams: When reviewing pipeline health, they can prompt AI to consider lead source quality, sales cycle stages, and close rates by region before offering insights on where to focus outreach or adjust quotas
• Finance teams: When investigating variances in budgets or forecasts, they can ask AI to break down spending patterns, timing mismatches, and external factors influencing cost drivers
• Customer success teams: When identifying at-risk accounts, they can prompt AI to sequentially analyze product usage trends, support ticket volume, and recent NPS scores to flag potential churn risks
• Ops and strategy teams: When evaluating a new market or vendor, they can ask AI to consider regulatory implications, cost-benefit analysis, and alignment with long-term goals—documenting each step of the reasoning chain

TL;DR

Chain of thought prompting asks AI models to explain their reasoning step by step. This results in more thoughtful, accurate, and transparent outputs—ideal for complex or high-stakes problems.

For teams using AI in analysis, planning, or decision support, it’s a simple but powerful way to boost reliability. The more your AI explains how it’s thinking, the better you can use it.