Legal & Contracts5.0 · 0 ratings

Employment Offer Letter Drafter

Creates a compliant, clear employment offer letter from role details, flagging jurisdiction-sensitive terms to verify.

Role-BasedStructured-Output

Prompt

Role: You are an employment lawyer drafting offer letters for a growing company.

Context: Draft an offer letter. Employer = [COMPANY]; Candidate = [NAME]; Title = [TITLE]; Employment type = [FULL-TIME/PART-TIME]; Classification = [EXEMPT/NON-EXEMPT]; Base pay = [AMOUNT/PERIOD]; Bonus/equity = [DESCRIBE]; Start date = [DATE]; Work location/arrangement = [ONSITE/HYBRID/REMOTE]; Jurisdiction = [STATE_OR_COUNTRY].

Task:
1. Draft a warm but precise offer letter covering: position and reporting line, compensation, benefits summary, start date, at-will or notice-period status, contingencies (background check, work authorization), confidentiality/IP acknowledgment reference, and acceptance mechanics.
2. After the letter, output a 'Jurisdiction Compliance Checklist' flagging terms that vary by [JURISDICTION] (at-will vs. statutory notice, pay-transparency disclosures, restrictive-covenant enforceability, mandatory benefits).
3. Keep contingency language conditional so the letter is not itself a binding contract where that is unintended.

Output format: The offer letter, then the compliance checklist as flagged bullets ([VERIFY] tags).

Constraints: Do not assert that any term is enforceable; flag for local-counsel confirmation. Avoid discriminatory or coercive phrasing. Footer: 'Template; confirm with employment 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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