Gemini vs Grok: Which Is Better?

Chain-of-thought prompting is one of the most consistently effective techniques for hard, multi-step problems, and it’s simpler to use than the name suggests.

Rather than asking a model to jump straight to a final answer, this technique asks it to reason through the problem step by step first, then answer. For math, multi-step logic, or complex tradeoffs, this consistently catches errors a straight-to-the-answer response would miss.

Generating intermediate reasoning steps gives a model the equivalent of showing its work, which makes errors in one step more likely to get caught before compounding into the final answer, and easier for you to spot where a flawed conclusion went wrong.

Many current reasoning models do this automatically. It still helps to request it explicitly with faster, non-reasoning variants tuned for speed, or when you want to see the intermediate steps directly.

Add “think through this step by step before answering” to a genuinely complex prompt. For simple questions, this just adds length without improving accuracy. See our full collection of prompt examples for more.

See Anthropic’s own documentation for more technical depth.