The Correct Way to Use Chain-of-Thought Prompting: Avoiding Common Pitfalls
Published:
I recently attended an AI in Finance conference and was surprised to discover that many researchers are using chain-of-thought (CoT) prompting incorrectly. This powerful technique can significantly improve reasoning in LLMs—but only when implemented properly. Let’s clarify the right approach, especially in zero-shot settings.
What is Zero-Shot Chain-of-Thought?
Zero-shot CoT involves two distinct rounds of prompting without using any task-specific examples. First, you prompt the model to generate step-by-step reasoning. Then, in a second round, you explicitly ask for the final answer based on that reasoning. This differs from few-shot CoT, which includes labeled examples.
Example Question
Consider the question:
“A company just announced a 20% dividend increase while simultaneously reporting declining revenues. Is this news good or bad?”
Incorrect Single-Stage Approach (Common Mistake)
# WRONG IMPLEMENTATION
response = llm.generate(
prompt="Let's think step by step: A company just announced..."
)
# Output includes both reasoning AND final answer in one response
Correct Zero-Shot CoT Approach
# STEP 1: Trigger reasoning
reasoning_prompt = "Q: A company announced a 20% dividend increase but declining revenues... A: Let's think step by step"
intermediate_response = llm.generate(reasoning_prompt)
# STEP 2: Request final answer
answer_prompt = f"""
Based on this analysis: '{intermediate_response}'
Is the news good or bad? Answer ONLY 'good' or 'bad'"""
final_answer = llm.generate(answer_prompt)
Why This Matters
- Prevents answer bleeding: Separating reasoning from final output avoids biasing the model’s explanation
- Improves transparency: You get a clean, auditable chain of logic
- Reduces hallucination: Clear separation minimizes speculative or fabricated conclusions
Explainable Prompting vs. Chain-of-Thought Prompting
Although both aim to improve interpretability, they differ in structure and use case:
| Feature | Explainable Prompting | Chain-of-Thought Prompting |
|---|---|---|
| Goal | Provide human-readable justification | Encourage structured reasoning |
| Output Structure | Single response with embedded rationale | Two-step: reasoning then answer |
| Best For | Summaries, end-user reports | Complex logic, analysis |
| Process | “Explain why…” style prompts | Sequential prompt-response flow |
| Example | “Explain why this is a strong argument” | “Solve this logic puzzle step-by-step” |
Key Differences:
- Explainable prompting focuses on self-contained narratives.
- CoT emphasizes decomposing logic from decision-making.
- Use Explainable Prompting for clear summaries, and CoT when correctness and traceability are essential.
Key Implementation Rules
- Do not include “so the answer is…” in the initial reasoning prompt
- Always split into two prompts/responses
- Validate or sanitize intermediate reasoning if needed
References
- Wei, J. et al. (2022). Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
- Kojima, T. et al. (2022). Large Language Models are Zero-Shot Reasoners
