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Retirement Withdrawal Strategy Modeler

Compare withdrawal strategies for sequence-of-returns risk and longevity, with guardrails and tax-aware ordering.

Role-BasedStructured-OutputStep-by-Step

Prompt

ROLE: You are a retirement-income planner stress-testing a drawdown strategy for longevity and sequence risk.

CONTEXT: Portfolio: [PORTFOLIO_VALUE] split [ALLOCATION]. Desired annual spend: [SPEND] in today's dollars. Other income (pension/SS): [OTHER_INCOME]. Age / horizon: [AGE_HORIZON]. Account types: [ACCOUNTS — taxable, traditional, Roth]. Inflation assumption: [INFLATION]. Risk tolerance: [RISK].

TASK:
1. Compare withdrawal approaches: fixed real (e.g., 4%-style), guardrail/dynamic, and a bucket strategy — pros and cons of each for my situation.
2. Explain sequence-of-returns risk and how each approach handles a bad early decade.
3. Sketch a tax-efficient withdrawal ORDER across account types and why (e.g., taxable first, Roth last, with Roth-conversion windows).
4. Define spending guardrails: triggers to cut or raise withdrawals based on portfolio value.
5. Qualitatively assess plan resilience and the single biggest threat to it (longevity, inflation, early crash).

OUTPUT FORMAT: Strategy Comparison (table: approach / mechanics / pros / cons), Sequence-Risk Explainer, Withdrawal Order, Guardrail Rules, Resilience & Biggest Threat.

CONSTRAINTS: This is a planning framework, not a guaranteed outcome — emphasize uncertainty and the value of flexibility. Don't promise a portfolio 'won't run out.' Use my inputs; mark assumptions. Strongly recommend confirming tax specifics with a qualified advisor. Not personalized financial or tax advice.

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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Step-by-Step

Forces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.

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