In today's AI landscape, understanding the distinction between conversational Large Language Models (LLMs) and reasoning models is crucial for effective application.

Conversational Models: Informal Role-Based Prompts

Conversational models, such as OpenAI's GPT*, Claude, and Llama, excel when prompted with persona or role-based instructions. For instance, the prompt:

I want you to act as a financial advisor and provide investment advice.

This approach leverages the model's extensive training in human-like dialogue, resulting in engaging and contextually appropriate responses. These models are ideal for tasks that require fluent, natural language generation without the necessity for detailed step-by-step reasoning.

* Note: It is important to understand that ChatGPT is the interface that run on various underlying models (e.g., GPT‑3.5, GPT‑4, GPT‑4o, GPT‑4o mini). And GPT‑4o, for instance, is not an independent competitor but a variant powering ChatGPT that enables multimodal capabilities (voice, vision, text) AND enhanced reasoning.

Reasoning Models: Structured, Step-Driven Prompts

In contrast, newer reasoning models—such as OpenAI’s o1/o3, Google's Gemini 1.5 Pro and DeepSeek R1—are built to tackle multi-step problems in math, coding, or research by integrating internal “chain‑of‑thought” capabilities so that, rather than simply imitating conversation, they work through problems step by step.

Best Practices for Prompting Reasoning Models

Recent research and experiments on prompting reasoning models advocate for:

• Step-by-Step Instructions: Encourage a chain-of-thought by including directives such as "Break the problem into steps" or "Explain each step." This method promotes logical progression and thoroughness in the model's responses.
• Directness over Roleplay: Instead of assigning a role, instruct the model to solve or explain problems step by step. This straightforward approach leverages the model's reasoning capabilities without the need for simulated personas.
• Clear Delimiters: Incorporate symbols or clear markers (e.g., "Step 1:", "Step 2:") to signal transitions in reasoning. This organization helps the model focus on breaking the problem into manageable parts, enhancing clarity and coherence.
• Minimal Context: Keep instructions concise to avoid overwhelming the model with unnecessary information. Brevity ensures that the model concentrates on the core task without distractions. Overloading the prompt with excessive details can hinder performance; simplicity often yields better results. For complex tasks, consider including a brief example that demonstrates the desired structure without overloading the prompt.
• Structured Output: For longer tasks, define clear sections (e.g., Introduction, Analysis, Conclusion) to guide the model's response. Such structuring aids in organizing complex information systematically.
• Test and Iterate: As models are evolving, experiment with prompt variations to find the optimal balance between instruction detail and model freedom.

Implementing these practices can enhance the performance of reasoning models in complex tasks, leading to more accurate answers.

Comparative Examples

Math & Coding Help

Conversational Model Prompt

I need help with this problem: [Problem]. Can you explain how to solve it?

Reasoning Model Prompt

Task: Solve the following problem: [Insert problem].
Instructions:
- Break the problem into clear, sequential steps.
- Briefly explain each step.
- Provide the final answer after demonstrating your reasoning.

Key Difference:

The conversational prompt elicits a friendly explanation, while the reasoning prompt directs the model to detail each step for clarity.

Research & Reports

Conversational Model Prompt

Summarize what you know about [Topic] in a clear, engaging way.”

Reasoning Model Prompt

Task: Prepare a detailed report on [Topic].
Instructions:
Introduction: Offer a concise overview.
Body: Present key findings with evidence and citations; analyze implications.
Conclusion: Summarize the main insights.

Key Difference:

The conversational approach provides a general summary, whereas the reasoning prompt organizes the response into structured sections for a comprehensive report.

Ethical Dilemma

Conversational Model Prompt

What are your thoughts on this ethical dilemma: [Dilemma]? Share your opinion.

Reasoning Model Prompt

Task: Analyze the ethical dilemma: [Insert dilemma].
Instructions:
- List the key ethical considerations.
- Evaluate multiple perspectives and explain the reasoning behind each.
- Conclude with a justified recommendation.

Key Difference:

The conversational prompt invites a general opinion, while the reasoning prompt ensures a step-by-step, in-depth analysis with a balanced conclusion.

Reasoning Prompts Templates

Math & Coding Help

Task: Solve the following problem.
Problem: [Insert the math or coding problem here]
Instructions:  
- Break the problem into clear, sequential steps.
- Briefly explain each step.
- Provide the final answer only after demonstrating your reasoning.

Research & Reports

Task: Prepare a detailed report on [Topic].
Instructions:
Introduction: Offer a concise overview of [Topic].
Body:
 - Present key findings with evidence and citations.
 - Analyze implications and contextual details.
Conclusion: Summarize the main insights.
Ensure the response is well-organized, fact-based, and structured in clear academic language.

Why it works: This structured prompt guides reasoning models to produce long-form, organized content. By breaking the output into sections, it reduces ambiguity and taps into the model’s long-context reasoning capabilities.

Conclusion

Conversational LLMs thrive on role-assignment and informal dialogue, but the new generation of reasoning models demands structured, explicit prompts that instruct them to “think aloud” and work through tasks step by step. By shifting from “I want you to act as…” to direct, structured prompts—such as those outlined above — you can better harness the enhanced reasoning capabilities of models like OpenAI’s o1, Google’s Gemini 1.5 Pro, and DeepSeek R1.

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