Chain of Thought Prompting Examples 2026: 10 Prompts That Improve LLM Reasoning

The 2022 Wei et al. paper showed CoT dramatically improves reasoning. Here are working examples you can copy today.

## Chain of Thought Prompting Examples 2026: 10 Prompts That Improve LLM Reasoning Chain of thought (CoT) prompting asks the model to reason step by step before answering. The technique comes from the 2022 paper "Chain-of-Thought Prompting Elicits Reasoning in Large Language Models" (Wei et al., Google Research), which showed large gains on math and logic benchmarks. In 2026 it is still the highest-leverage prompting skill. ## Why It Works Models trained to predict the next token produce better final answers when they generate intermediate reasoning. The paper reported gains of up to +18% accuracy on GSM8K math problems simply by adding "Let's think step by step." ## 10 Copy-Paste Examples **1. Arithmetic** > A store sells apples at $2 each and offers 15% off for 12 or more. If I buy 15 apples, how much do I pay? Let's think step by step. **2. Logic** > All roses are flowers. Some flowers fade quickly. Is it guaranteed that some roses fade quickly? Think through this carefully before answering. **3. Coding** > Write a function that checks if a string is a palindrome. First explain the algorithm, then write the code. **4. Math word problem** > A train leaves at 9:00 traveling 80 km/h. Another leaves at 10:00 at 100 km/h. When does the second catch up? Show your work. **5. Decision making** > I have $500/month to invest. Compare index funds, bonds, and CDs step by step, then recommend one. **6. Data analysis** > Here is a sales table by quarter. Walk through the trend analysis step by step before concluding. **7. Translation with constraints** > Translate this paragraph to French, keeping technical terms in English. Reason about the tricky terms first. **8. Fact-checking** > Verify this claim about battery technology. Break it into sub-claims and assess each. **9. Planning** > Plan a 3-day trip to Tokyo on a $400 budget. Reason about costs in order. **10. Debugging** > This Python code throws a KeyError. Trace through the execution line by line and find the bug. ## Advanced Variant: CoT + Few-Shot Combine with examples for best results: > Example: "Q: 17*24? A: 17*20=340, 17*4=68, 340+68=408. Answer: 408." > Now solve: 23*37? ## FAQ **Does CoT work on small local models?** Partially - models under 7B often cannot sustain long reasoning chains. Use it mainly with capable models. **Is "think step by step" enough?** It helps, but explicit structure (numbered steps, "show your work") works better than the magic phrase alone. **What about tree of thought?** ToT (Yao et al. 2023) explores multiple reasoning branches - better for open-ended problems but needs more tokens and often an agent framework.