Build a contract negotiation playbook with fallback positions
Creates a clause-by-clause playbook with preferred, fallback, and walk-away positions so business teams and junior lawyers negotiate consistently.
The prompt
Help me build a negotiation playbook for one of our standard contract types.
Contract type: [e.g. "SaaS customer agreement", "supplier MSA", "reseller agreement"]
Our usual side: [customer / vendor / both]
Our template (or key clauses): [PASTE]
Clauses counterparties push back on most: [LIST]
Business risk appetite: [e.g. "conservative on liability, flexible on payment terms"]
Who will use the playbook: [lawyers / contract managers / sales]
Governing law(s): [JURISDICTIONS]
For each clause, give:
- Why it matters to us, in one or two plain sentences
- Preferred position (our template language)
- Fallback 1 and Fallback 2, each with sample clause language
- Walk-away / must-escalate point
- Who can approve each level (e.g. contract manager, legal, CFO)
- Common counterparty arguments and a short response to each
Cover at least: limitation of liability and carve-outs, indemnities, IP ownership and license, confidentiality, data protection, warranties, termination, payment terms, assignment, governing law and disputes. Add others relevant to this contract type.
Output as a table per clause, then a one-page quick-reference grid (Clause | Preferred | Fallback | Escalate). Mark anything that depends on local law with [CONFIRM FOR JURISDICTION]. This is a drafting aid; a qualified lawyer must review and approve the positions before the team relies on them.
Highlighted [brackets] are blanks to replace with your own details.
Fill in the blanks and use this promptMore Legal & Compliance prompts
What to fill in
- customer / vendor / both
- PASTE
- LIST
- e.g. "conservative on liability, flexible on payment terms"
- lawyers / contract managers / sales
- JURISDICTIONS
- CONFIRM FOR JURISDICTION
How this prompt is built
This prompt follows the Few-Shot structure (Few-shot prompting). A few-shot prompt includes one or more worked examples of the input and the output you want, so the model can copy the pattern instead of guessing it.
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