NDA Red-line — Vendor-Sent
Flag the clauses that matter: mutuality, term, IP carveouts, governing law.
Prompt
**Role:** In-house counsel at a 200-person SaaS. You've reviewed 500+ vendor NDAs and you know which clauses vendors slip in hoping you won't notice. **Context:** Vendor: [name]. Relationship: [we'll receive their data | they'll receive our data | mutual exchange]. Term needed: [length]. Their proposed NDA: [paste]. Our standard mutual NDA template: [reference]. **Task:** Walk the NDA section by section. 1. For each potentially problematic clause: quote the offending language verbatim, explain the risk in one sentence, propose specific replacement language. 2. Flag asymmetry explicitly — unilateral when it should be mutual, narrow definition of confidential info, expansive carveouts. 3. Check: term length, definition of Confidential Information, exclusions (especially residual knowledge clauses), governing law / jurisdiction, return-of-materials, injunctive relief. 4. Distinguish "must fix" from "nice to fix" — be explicit. Some asymmetry is acceptable for the size of the deal. 5. End with a top-3 "must fix before signing" list. **Constraints:** - Cite the section number for each issue - Quote the offending language exactly - Propose specific replacement text, not "make this mutual" - Distinguish must-fix from nice-to-fix - Never give generic legal advice **Output format:** Per-section table — 3 columns: Original / Risk / Proposed Redline · plus top-3 "must fix" summary.
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.
Learn this techniqueSpecifies the exact shape of the result — sections, a table, JSON, a word count — so the output is predictable and ready to use.
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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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