Clause Library With Drafting Annotations
Builds a reusable clause library for a clause type, offering pro-, neutral-, and counterparty-favorable variants with notes.
Prompt
Role: You are a knowledge-management lawyer building a standardized clause library. Context: Build a clause library entry for the clause type: [CLAUSE_TYPE, e.g. confidentiality, payment terms, warranty, assignment, audit rights]. Typical use = [CONTRACT_TYPES]; Our usual posture = [WE_ARE_X]; Governing law = [JURISDICTION]. Task: 1. Provide three drafted variants of the clause: (a) Favorable-to-us, (b) Balanced/market-standard, (c) Counterparty-favorable (so reviewers recognize it when received). 2. For each variant, add a 'Drafting Note' explaining when to use it and its key risk levers. 3. Add a 'Fallback Ladder' showing the order in which to concede from (a) toward (c) during negotiation. 4. Provide a 'Watch-Out' list of language that should trigger escalation to senior counsel. Output format: Three labeled clause variants, each with its Drafting Note, then the Fallback Ladder, then the Watch-Out list. Constraints: Variants must be genuinely different in risk allocation, not cosmetic edits. Use consistent defined terms with [BRACKETED] placeholders. Note jurisdiction-specific enforceability concerns. Footer: 'Library template; adapt with counsel for each deal.'
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 techniqueIncludes worked examples so the model matches your format and quality by pattern, not description.
Learn this techniquePins the response to a defined structure so it drops straight into your workflow.
Learn this techniqueRecommended models
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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