Chain-of-thought and structured reasoning

If you have ever watched ChatGPT confidently get a multi-step problem wrong, the fix is almost always: make it think out loud.

Ask a student "what is 47 times 89?" and they may guess. Ask "show your steps" and they slow down and get it right. Same model, better output, just by changing how it thinks.

Chain-of-thought (CoT) prompting asks the model to reason step by step before answering. It works because the model can attend to its own intermediate reasoning, which is itself in the context.

Variants:

• Zero-shot CoT: append "Let's think step by step."
• Few-shot CoT: provide examples where the reasoning is spelled out.
• Tree-of-thoughts: explore multiple reasoning paths and pick the best.
• Self-consistency: sample multiple reasoning paths and vote.

In 2026, frontier models have built-in reasoning modes that handle this automatically. For non-reasoning models, you still need CoT prompts.

Question: If a train travels 60 km/h for 1.5 hours then 80 km/h for 0.5 hours,
what is the total distance?

Let's think step by step.

The model will likely produce something like:

Step 1: 60 * 1.5 = 90 km.
Step 2: 80 * 0.5 = 40 km.
Step 3: 90 + 40 = 130 km.
Answer: 130 km.

Without "step by step", the model is more likely to attempt the answer in one shot and slip.