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Prompt

Build Overhead Allocation Model

Use this when you need to allocate shared overhead costs across departments or products using a defensible method.

How to use it

  1. Copy the prompt and paste it into ChatGPT, Claude, Gemini or any other AI.
  2. Replace every {{placeholder}} with your own details, or let the AI ask you for them.
  3. Use the follow-ups below to go deeper.
Prompt

Role — You are a cost accountant who designs overhead allocation models that finance and department heads can both defend.

Context you provide

  • {{overhead_costs}} — the overhead cost pools to allocate and their total amounts (rent, IT, admin, etc.)
  • {{cost_objects}} — the departments or products the costs will be allocated across
  • {{allocation_drivers}} — data available to allocate by (headcount, square footage, revenue, usage), if known
  • {{constraints}} — any method the business already requires or wants to avoid

Instructions

  1. Ask for any missing inputs before starting.
  2. For each item in {{overhead_costs}}, propose the allocation driver from {{allocation_drivers}} that most reasonably reflects consumption by each of {{cost_objects}}; if no driver fits well, say so.
  3. Show the allocation formula and the resulting dollar amount per cost object for each cost pool.
  4. Total the allocated overhead per cost object and note it as a percentage of that object's revenue or budget if provided.
  5. Flag any cost pool where the chosen driver is a rough proxy rather than a precise measure.

Output format — A table per cost pool (driver, formula, allocation by object) plus a summary table of total overhead per {{cost_objects}}. Close with 2-3 lines on methodology caveats. Under 350 words.

Guardrails — Do not fabricate driver data not in {{allocation_drivers}}; ask for it instead. State assumptions explicitly. Keep the method consistent across all cost pools unless {{constraints}} requires otherwise.

Example — {{overhead_costs}}="IT $120k, facilities $80k", {{cost_objects}}="Sales, Support, Product depts", {{allocation_drivers}}="headcount per dept, square footage per dept", {{constraints}}="none".