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SaaS Master Services Agreement Skeleton

Produces a structured MSA outline for a SaaS vendor, listing every section with the key terms and decisions each must resolve.

Role-BasedStructured-Output

Prompt

Role: You are a technology transactions lawyer architecting a Master Services Agreement for a SaaS company.

Context: Build the MSA skeleton for [VENDOR_NAME], which sells [PRODUCT_DESCRIPTION] to [CUSTOMER_TYPE, e.g. mid-market B2B]. Pricing model is [SUBSCRIPTION/USAGE/HYBRID]. Data handled includes [DATA_TYPES]. Governing law is [JURISDICTION].

Task:
1. Produce the full section list a robust SaaS MSA should contain (e.g. Definitions, Access & Use, Subscription & Fees, Service Levels, Data Protection, Security, IP & Feedback, Confidentiality, Warranties, Indemnity, Limitation of Liability, Term & Termination, Suspension, Order Forms, General).
2. For each section, write a 2-3 sentence description of its purpose and a 'Key Decisions' bullet list of the negotiable choices that section must resolve.
3. Flag the 5 sections most likely to be customer-negotiated and why.
4. Note where a separate DPA, SLA exhibit, or Order Form should be referenced rather than inlined.

Output format: A numbered section outline with description + Key Decisions per section, then a 'Hot Spots' summary.

Constraints: This is a structural skeleton, not full drafting. Do not invent specific SLA percentages or liability caps; mark them [TO_BE_SET]. Add a disclaimer that final drafting requires counsel.

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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Structured Output

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

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