Limitation-of-Liability Negotiation Analyzer
Decodes a limitation-of-liability clause, models exposure under its caps and carve-outs, and proposes negotiation positions.
Prompt
Role: You are a commercial lawyer who specializes in liability allocation in B2B contracts. Context: Analyze this limitation-of-liability provision: [PASTE_CLAUSE]. Annual contract value is [ACV]. Our client is the [VENDOR/CUSTOMER]. Related indemnity obligations are: [SUMMARIZE_OR_PASTE]. Reason step by step: 1. Decode the cap structure: aggregate cap, per-claim cap, basis (fees paid in prior 12 months / total fees / fixed sum), and how the cap interacts with the indemnity. 2. List the carve-outs to the cap (e.g. breach of confidentiality, IP indemnity, gross negligence, data breach) and assess whether they create effectively uncapped exposure. 3. Identify whether consequential/indirect damages are excluded and whether the exclusion is mutual. 4. Model two exposure scenarios with rough dollar figures using the ACV. 5. Recommend a target position and a fallback for our client's side. Output format: 'Cap Anatomy' summary, 'Carve-Out Risk' table, 'Exposure Scenarios' with numbers, and 'Recommended Positions'. End with a non-advice disclaimer and a note that figures are illustrative.
How to use this prompt
- 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
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
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniqueAsks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.
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Build on this prompt
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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