Plan an equity refresh for key employees
A fair, budgeted plan for refresh grants to retain key people, with criteria, sizing logic, and how to explain it.
The prompt
Help me plan equity refresh grants to retain key employees.
Company stage and valuation context: [e.g. "Series B, last valuation $X, next round expected in 18 months"]
Equity pool available: [shares or % remaining, and any planned top-up]
Employees under consideration: [ROLE, level, tenure, % vested, original grant value, performance, flight risk - anonymize names]
Problem we're solving: [e.g. "early employees fully vested", "underwater options after down round", "competitive offers"]
Current equity guidelines: [if any]
Do this:
1. Explain the common refresh approaches (annual merit grants, vesting-cliff top-ups, promotion grants, broad-based refresh) and which fits my problem.
2. Propose eligibility criteria that are consistent and defensible, not based on who asks loudest.
3. Propose a sizing method (e.g. a percentage of the original grant or a target value by level and performance), and show how much of the pool it uses.
4. Apply it to my list in a table: Employee | Level | % vested | Unvested value | Proposed grant | Rationale. Show the math.
5. Model dilution and remaining pool after the refresh and planned hiring.
6. Check for fairness issues: gaps by gender, race, or other protected characteristics in similar roles, and refreshes that reward only the vocal.
7. Draft talking points managers can use to explain the grant and what equity is (and isn't) worth.
This is general information, not tax or legal advice. Option pricing, valuations (e.g. 409A in the US), tax treatment, and board approval should be handled with your lawyer and tax advisor.
Highlighted [brackets] are blanks to replace with your own details.
Fill in the blanks and use this promptMore HR & Hiring prompts
What to fill in
- shares or % remaining, and any planned top-up
- if any
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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