Chain-of-Thought and Reasoning Prompts AI Prompt

Design chain-of-thought (CoT) and structured reasoning prompts for complex tasks.

Task type: {{task_type}} (math, logic, multi-step analysis, classification with rationale)
Model: {{model}}
Accuracy requirement: {{accuracy}} (standard or high-stakes)

1. Zero-shot chain-of-thought:
   Simply adding 'Let's think step by step.' to the prompt dramatically improves accuracy on multi-step reasoning tasks.

   Template:
   'Solve this problem: {{problem}}
   Let's think step by step. Show your reasoning before giving the final answer.'

   For even more structure:
   'Work through this problem systematically:
   1. Identify the key information given
   2. Determine what needs to be found
   3. Apply the relevant principles step by step
   4. State the final answer clearly

   Problem: {{problem}}'

2. Few-shot CoT:
   Provide 2-3 worked examples before the target problem.
   Each example shows: input → reasoning steps → output

   Format:
   'Q: [example problem]
   A: Let me think step by step.
   Step 1: ...
   Step 2: ...
   Therefore: [answer]

   Q: [target problem]
   A: Let me think step by step.'

   Example quality: examples should cover different reasoning patterns, not just the same type repeated.

3. Self-consistency:
   - Generate N independent responses to the same question (different random seeds / temperature > 0)
   - Aggregate by majority vote on the final answer
   - Empirically improves accuracy by 5-10% on reasoning benchmarks
   - Practical implementation: run the prompt 5 times, take the most common answer

4. ReAct (Reasoning + Acting):
   - Interleave: Thought → Action → Observation loops
   - The model reasons about what to do, takes an action (tool call), observes the result, repeats
   - Use for: tasks requiring external tool use, multi-step information retrieval, code execution

   Format:
   'Thought: I need to find the current population of France.
   Action: search("France population 2024")
   Observation: France has a population of approximately 68 million.
   Thought: Now I can answer the question.
   Answer: France's population is approximately 68 million.'

5. Least-to-most prompting:
   - Decompose the hard question into simpler sub-questions
   - Solve each sub-question sequentially, feeding prior answers as context
   - Use for: compositional tasks, multi-hop questions

Return: CoT prompt template for this task, few-shot examples, self-consistency implementation plan, and reasoning format specification.