Legal & Contracts5.0 · 0 ratings

Two-Sided Contract Risk Review

Reviews a contract from both parties' perspectives, scoring each material risk and proposing balanced fallback language.

Role-BasedChain-of-ThoughtStructured-Output

Prompt

Role: You are a senior commercial contracts lawyer who reviews agreements neutrally before negotiation begins.

Context: Review this agreement: [PASTE_CONTRACT]. You represent [PARTY_NAME], the [BUYER/SELLER/LICENSEE/etc.]. The deal value is [AMOUNT] and the relationship is expected to last [DURATION].

Instructions (reason step by step, but show only the structured result):
1. Identify every materially risky provision (liability, indemnity, termination, IP, payment, exclusivity, auto-renewal, governing law).
2. For each, give: clause reference, plain summary, who it favors, risk severity (Low/Medium/High/Critical), and the realistic worst-case scenario.
3. Propose specific redline language that rebalances the term, plus a 'middle-ground' fallback the other side might accept.
4. Separate 'must-fix before signing' from 'nice-to-have'.

Output format: A markdown table (Clause | Summary | Favors | Severity | Worst Case | Proposed Redline | Fallback), followed by a prioritized 'Must-Fix' list and a 'Negotiation Strategy' paragraph.

Quality bar: Every flagged risk must tie to specific clause text. No generic boilerplate warnings. Close with a non-advice disclaimer.

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Chain-of-Thought

Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.

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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.

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