Chain-of-Thought (CoT) prompting represents one of the most significant breakthroughs in prompt engineering, enabling AI models to tackle complex reasoning tasks by breaking them down into manageable steps. However, in 2026, CoT techniques work differently across modern models—particularly with the emergence of dedicated reasoning models.
What is Chain-of-Thought Prompting?
CoT prompting involves explicitly encouraging the model to show its reasoning process, step by step, before arriving at a final answer. Instead of jumping directly to conclusions, the model "thinks aloud."
Basic Example
Without CoT:
Q: A store has 15 apples. They sell 7 in the morning and 4 in the afternoon. How many apples are left?
A: 4 apples
With CoT:
Q: A store has 15 apples. They sell 7 in the morning and 4 in the afternoon. How many apples are left?
A: Let me think step by step:
1. The store starts with 15 apples
2. They sell 7 in the morning: 15 - 7 = 8 apples left
3. They sell 4 in the afternoon: 8 - 4 = 4 apples left
Therefore, 4 apples are left.
Types of Chain-of-Thought Prompting
1. Few-Shot CoT
Provide examples with reasoning steps:
Q: Roger has 5 tennis balls. He buys 2 more cans of tennis balls. Each can has 3 tennis balls. How many tennis balls does he have now?
A: Roger started with 5 balls. 2 cans of 3 tennis balls each is 6 tennis balls. 5 + 6 = 11. The answer is 11.
Q: The cafeteria had 23 apples. If they used 20 to make lunch and bought 6 more, how many apples do they have?
A: Let me work through this step by step:
1. Started with: 23 apples
2. Used for lunch: 20 apples
3. Remaining after lunch: 23 - 20 = 3 apples
4. Bought 6 more: 3 + 6 = 9 apples
The answer is 9.
2. Zero-Shot CoT
For standard instruction models like GPT-4o, simply add "Let's think step by step":
Q: If a train travels 60 miles per hour for 2.5 hours, how far does it travel?
A: Let's think step by step:
3. Structured CoT
Use explicit structure for complex problems:
Problem: [State the problem]
Given information: [List what we know]
What we need to find: [State the goal]
Step-by-step solution:
1. [First step]
2. [Second step]
3. [Continue...]
Final answer: [Conclusion]
CoT and Reasoning Models in 2026
A critical update for 2026: reasoning models work fundamentally differently from instruction models when it comes to CoT.
For Instruction Models (GPT-4o, Claude 3.7 Sonnet, Gemini 2.0 Flash)
Use explicit CoT prompts exactly as shown above. These models benefit significantly from your cues to "think step by step" or show reasoning. The technique is as powerful as ever.
For Reasoning Models (OpenAI o1, o3, o3-mini)
Do NOT use "think step by step" or other explicit CoT prompts. These models have built-in reasoning phases that happen internally before they generate output. Adding CoT instructions can actually interfere with their native reasoning process.
Instead, for o1/o3 models, simply state the problem clearly:
Q: If a train travels 60 miles per hour for 2.5 hours, how far does it travel?
The model will internally perform extended reasoning and then show you the result. If you need to see the reasoning, these models have a "thinking" output mode (in supported APIs) that displays their internal reasoning process.
When to Use Chain-of-Thought
Effective for:
- Mathematical problems: Arithmetic, algebra, word problems (especially on instruction models)
- Logical reasoning: Puzzles, deductions, analysis
- Multi-step processes: Procedures, algorithms, workflows
- Complex analysis: Breaking down complicated scenarios
- On instruction models (GPT-4o, Claude 3.7 Sonnet): CoT is highly effective
- On reasoning models (o1/o3): Let the model reason internally; use CoT only if explicitly showing work is required for your use case
Less effective for:
- Simple factual questions: Direct recall tasks
- Creative writing: Open-ended generation
- Opinion-based responses: Subjective judgments
Advanced CoT Techniques
Tree of Thoughts
Exploring multiple reasoning paths (use on instruction models):
Let me explore different approaches:
Path 1: Direct calculation
- Step 1: ...
- Step 2: ...
Path 2: Alternative method
- Step 1: ...
- Step 2: ...
Comparing paths: [Analysis]
Best approach: [Selection]
In 2026, some agentic frameworks now implement Tree of Thoughts natively, allowing models to explore multiple reasoning branches autonomously.
Self-Consistency
Generate multiple CoT reasoning paths and select the most common answer. This technique works well for instruction models to improve robustness.
Verification Steps
Include checking mechanisms:
Solution: [Steps]
Verification: Let me double-check this...
Alternative check: Does this make sense?
Best Practices
1. Clear Step Indicators
- Use numbers, bullets, or clear transitions
- Make each step logically follow from the previous
2. Explicit Reasoning
- Don't skip steps
- Explain why each step follows
- Show calculations clearly
3. Structured Format
Understanding the problem: [Interpretation]
Available information: [Given data]
Approach: [Method selection]
Step-by-step solution:
1. [Step with reasoning]
2. [Step with reasoning]
Verification: [Check]
Final answer: [Result]
Common Pitfalls
1. Skipping Steps
Bad:
A: The answer is obviously 15.
Good:
A: Let me calculate step by step:
1. First, I'll add the initial quantities...
2. Then subtract what was used...
3. Finally, add what was purchased...
The answer is 15.
2. Unclear Reasoning
Bad:
A: Some calculations... the answer is 42.
Good:
A: Let me work through this:
1. Initial amount: 50
2. Reduction of 16%: 50 × 0.16 = 8
3. New amount: 50 - 8 = 42
3. Wrong Model Selection
Bad:
Using o3 with: "Let's think step by step"
Good:
Using o3 with just the problem statement; using GPT-4o with: "Let's think step by step"
Measuring CoT Effectiveness
- Accuracy: Does the final answer improve?
- Consistency: Similar problems get similar reasoning
- Interpretability: Can humans follow the logic?
- Error identification: Where does reasoning break down?
- Model efficiency: For instruction models, does CoT improve results relative to token cost?
Chain-of-Thought prompting transforms AI from a "black box" into a transparent reasoning partner, making it invaluable for complex problem-solving tasks. The key to 2026 success is matching your CoT strategy to your model type: explicit for instruction models, implicit for reasoning models.
Continue Learning
Looking to put these techniques into practice? Check out these related resources:
- Few-Shot Learning in 2026: 7 Techniques That Make ChatGPT Smarter — Combine CoT with few-shot examples for even better results; learn when few-shot helps vs hurts.
- The Evolution of Prompt Engineering in 2026: From Basic Queries to Agentic AI — See where CoT fits in the broader landscape of prompting techniques and agents.
- How Large Language Models Work in 2026: A Practical Guide for Prompt Engineers — Understand the architecture and training methods that make CoT effective.
- Browse our free prompt library — 60+ ready-to-use prompt templates you can copy and paste.