Chain-of-Thought Prompting definition
Chain-of-thought prompting is a technique for getting better answers from large language models on multi-step problems by having the model work through intermediate reasoning steps before giving a final answer. It can be triggered with examples of worked solutions or a simple instruction such as think step by step, and it is built into today's reasoning models.
How chain-of-thought prompting works
Language models generate text one token at a time, and each token can only build on what came before. When a model jumps straight to an answer for a problem that needs several steps, such as a pricing calculation or a logic puzzle, it has no room to work things out. Asking it to write the intermediate steps first gives the model a scratchpad: each step becomes context for the next, and the final answer is conditioned on that reasoning.
Google researchers described the technique in 2022, showing that worked examples with reasoning in the prompt markedly improved large models on arithmetic, commonsense and symbolic reasoning tasks. A follow-up study found that simply adding let's think step by step to a question, with no examples at all, produced similar gains in many cases.
Zero-shot, few-shot and reasoning models
Chain of thought shows up in several forms today. The right one depends on the model you use, the cost you can accept and how much control you need over the format of the reasoning:
- Zero-shot CoT: an instruction such as think through this step by step before answering
- Few-shot CoT: two or three few-shot examples that demonstrate the reasoning style you want
- Structured CoT: reasoning in a fixed format, such as numbered checks, followed by a separate final answer field
- Self-consistency: sampling several reasoning paths and taking the most common answer
- Reasoning models: models from OpenAI, Anthropic, Google and DeepSeek trained to reason internally before responding, often with an adjustable thinking budget
Example: a support refund decision
Without chain of thought, a prompt might ask whether a customer is eligible for a refund, yes or no, and the model may answer confidently and wrongly. With it, the prompt asks the model to check each policy condition in turn: when the order was placed, whether the item was used, whether it was a sale item, and only then decide. The answer becomes more accurate and, just as important, reviewable, because a person can see which condition drove the decision.
In production, teams usually keep the reasoning internal and show users only the result, while logging the reasoning for debugging and evaluation. Pair the technique with prompt engineering basics such as clear instructions, explicit output formats and grounded context from your own documents.
Limitations and cost
Chain of thought is not a guarantee of correctness. Models can write plausible reasoning that does not reflect how they reached the answer, or reason carefully from a false premise, so outputs still need evaluation on real test cases. Longer outputs also mean more tokens, higher cost and higher latency, which adds up quickly at scale.
For simple classification, extraction or lookup tasks, chain of thought adds cost without much benefit. Use it where tasks genuinely involve several steps, comparisons or calculations. Nexzem tests prompts with and without explicit reasoning on client data during generative AI development and keeps whichever performs better for the cost.