How to prompt reasoning models (o3, Claude thinking, Gemini thinking)

Practical patterns for prompting chain-of-thought models: what to let the model figure out, what to specify, and what to avoid.

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What makes reasoning models different

Reasoning models (o3, Claude with extended thinking, Gemini thinking) run an internal chain-of-thought before producing output — they spend tokens on exploration before answering.
They handle ambiguous, multi-step tasks better than standard models, but require a different prompting style: less scaffolding from you, more clarity about what success looks like.
Explicit step-by-step prompting (like 'think step by step') is often redundant — the model already does this internally. Overloading it with process instructions can interfere with its own reasoning.

What to specify and what to leave to the model

Specify: the goal, the constraints, the output format, and what counts as a correct answer. Give it enough grounding to evaluate its own work.
Leave to the model: the reasoning path, intermediate steps, and how to decompose the problem. It handles this better than you can prescribe.
For factual or research tasks: require citations or grounded claims. Reasoning models can still hallucinate — they reason better, but they are not immune to confabulation.
For mathematical or logical tasks: ask for a final answer with a short justification, not a full narration of every step. Narrated steps can introduce errors in long chains.

Common mistakes

Over-scaffolding: adding 'First, list all options. Then, evaluate each. Finally, pick the best.' — the model already does this; your instructions can conflict with its own plan.
Under-specifying the goal: vague success criteria mean the model may reason well and still give you an unusable answer.
Ignoring output format: reasoning models can produce long, unstructured answers. Always specify the format you need, or you will spend time parsing the result.

Templates

Complex multi-step reasoning

Goal: [what a correct answer looks like — one sentence]
Context: [key facts, constraints, definitions]
Output format: [bulleted recommendation / decision table / ranked list]
Quality bar:
- Must include: [specific items]
- Must avoid: [forbidden claims or assumptions]
If the answer requires trade-offs, state them explicitly.

Opens on home with the prompt prefilled.

Open in Promptea

Structured analysis with justification

Analyze the following and return:
1. Final answer (1–2 sentences)
2. Key reasoning (max 3 bullets — cite the data)
3. Confidence: High / Medium / Low and why

Data:
"""[paste]"""

Constraints: [e.g. use only the provided data, no external assumptions]

Opens on home with the prompt prefilled.

Open in Promptea

FAQ

Should I still write 'think step by step' for reasoning models?

No. Reasoning models run an internal chain-of-thought automatically. Adding it is at best redundant and at worst disruptive. Focus on specifying the goal and output format clearly instead.

Are reasoning models more accurate for math and logic?

Generally yes for multi-step problems, but they still make arithmetic errors and can confabulate facts. Always verify numerical results and require citations for factual claims.