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Reasoning models are different from standard AI models. They automatically break down problems into steps internally before giving you an answer. Most major AI providers now offer reasoning-capable models. This means you need to adjust how you write prompts.

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What Makes Reasoning Models Different

Regular AI models respond immediately with their best guess. Reasoning models pause to think through the problem systematically before responding. They:
  • Analyze multi-step problems without being told how
  • Work through logical deductions on their own
  • Check their own work for consistency
  • Handle complex analysis better than standard models
The trade-off is they take longer to respond, but the quality is usually worth the wait for complex legal analysis.

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Key Prompting Changes

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Be Direct and Simple

Reasoning models don’t need extensive instructions about how to think. They already know how to break down problems. Standard model prompt: 
First, identify all liability provisions.
Then, assess each for unlimited exposure.
Finally, suggest appropriate caps.
Reasoning model prompt: 
Review the liability provisions and suggest appropriate caps where we have unlimited exposure.
The reasoning model will automatically identify, assess, and suggest – you don’t need to spell out the steps.

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Skip the Examples

These models work best with zero-shot prompting (no examples). Adding examples can actually confuse them or make them overthink simple tasks. Don’t do this: “Here are three examples of good liability caps…” Do this: “Ensure liability caps align with industry standards for SaaS vendors.”

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Control Output Formatting Explicitly

Reasoning models now avoid markdown formatting unless you specifically ask for it. If you want formatted output, say so. To get formatted output: 
Provide your analysis with markdown formatting enabled. Use headers, bullets, and tables where appropriate.
Or simply include “Formatting re-enabled” in your prompt.

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Provide Essential Context Only

These models can handle large documents, but don’t dump unnecessary background. Give them what matters for the specific task. Too much: Full company history, all previous negotiations, entire email chains Just right: Current document, your role, key constraints, specific question

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Best Practices

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Structure Your Input Clearly

Use XML tags or clear sections to organize different parts of your prompt: 
<context>
We're a startup vendor reviewing an enterprise MSA.
Low leverage situation.
</context>

<task>
Identify terms that could prevent us from raising our next funding round.
</task>

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Specify Output Preferences

Be explicit about what you want back: 
Provide:
- Three bullet points with the critical issues
- One paragraph explaining the business impact
- Table of suggested redlines

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Request Self-Checking

These models are good at verification. Ask them to check their own work: 
After your analysis, verify there are no contradictions between your recommendations.

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Control Reasoning Effort

Some platforms let you specify how hard the model should think: 
Use high reasoning effort to analyze this complex indemnification structure.

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Handle Ambiguity Upfront

Tell the model what to do with unclear situations: 
If any provisions are ambiguous, state your assumptions before analyzing.

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When to Use Reasoning Models

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Perfect For:

  • Complex multi-party agreements
  • Regulatory compliance analysis
  • Untangling contradictory provisions
  • Risk assessment across multiple documents
  • Novel legal issues without precedent

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Use Standard Models For:

  • Simple document summaries
  • Basic information extraction
  • Routine playbook applications
  • Quick yes/no questions

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Working with Large Documents

Reasoning models excel at large document analysis. Instead of breaking documents into chunks: 
Review this entire merger agreement for provisions that could delay closing. Focus on conditions precedent and termination rights.
The model will systematically work through the entire document.

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Common Pitfalls

  • Over-prompting: These models already know how to reason. Don’t micromanage the steps — define the problem and let the model find the best approach.
  • Too many examples: Unlike standard models, reasoning models work better figuring things out themselves. Examples can make them overthink.
  • Assuming formatting: If you want bullets, tables, or bold text, explicitly request it. Otherwise you’ll get plain text.
  • Rushing complex analysis: These models take longer but produce better results. Don’t shortcut the process with oversimplified prompts.

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The Key Insight

Reasoning models are like senior attorneys who already know how to approach legal analysis. Don’t treat them like junior associates who need step-by-step instructions. Give them the problem clearly and let them apply their training. Focus your prompts on defining the problem and desired output, not the process in between.