How to Move Your Personal Finance Data From ChatGPT to Maple — With This Prompt

AI assistants make budgeting, retirement planning, and credit-score repairs easier than ever—if you’re willing to hand them sensitive numbers. Tools such as ChatGPT, Grok, and Claude keep everything you type, from salaries to debts. That’s convenient, yet it also means your private data lives on someone else’s servers.

Maple AI flips the script: every conversation is end-to-end encrypted so only you can read or reuse your information. In this step-by-step guide you’ll learn how to:

1. See what money details ChatGPT already stores
2. Export those facts with one “Financial Export Prompt”
3. Import them into Maple with a matching “Financial Import Prompt”
4. Ask Maple for hyper-personalized but fully private advice on budgets, raises, retirement, and credit scores

Let’s jump in.

1. What ChatGPT (and other AI services) Remember About Your Money

Every question you’ve asked about budgeting, loans, or investments becomes part of ChatGPT’s long-term memory. That data can resurface in later chats—handy yet risky. Before switching to a privacy-first assistant, you need a clean export.

2. Craft a One-Shot Financial Export Prompt

Open any ChatGPT thread and paste the block below. It instructs the model to search its entire history, pull out personal-finance info only, and package it neatly.

You are an AI language model with full access to:
• The complete text of every message in this conversation
• Any long-term memory, user-profile, or retrieval tools provided by your platform
  (vector store, memory API, knowledge graph, etc.)

========================================================
TASK 1 – PERSONAL-FINANCE EXTRACTION
========================================================
Search *all* available data for explicit or implicit information about **my personal finances only**  
(e.g., salary or income figures, savings targets, personal budgets or spending caps, personal assets or liabilities).

❗ **Exclude** anything tied to a business I own, manage, or invest in—such as corporate budgets, revenue, funding terms, valuations, or ad spend paid from company accounts.

For **each** personal-finance data point you find, output four fields:

  • **Fact** – concise statement of the personal-finance item  
  • **Source** – date/timestamp or message-ID (or “stored-memory” if retrieved from a memory tool)  
  • **Evidence** – direct quotation or close paraphrase supporting the fact  
  • **Context** – one-sentence note explaining how/why that figure was mentioned

Rules:
1. Use only information that is clearly stated or unambiguously implied.  
2. Do **not** invent or guess missing numbers.  
3. Skip any item that is primarily business-related.  
4. If no personal-finance information exists, state “No personal financial information found” and skip Task 2.

Present the results as a bulleted list under the heading **“Personal Financial Facts Found.”**

========================================================
TASK 2 – TRANSFER PROMPT CONSTRUCTION
========================================================
After listing the facts, create a second block labelled **“Transfer-to-Another-LLM Prompt.”**  
This block lets me copy/paste all personal-finance facts into any other model.

Build it exactly like this (but filled in):

### Transfer-to-Another-LLM Prompt
You are an AI assistant that needs to store my current personal-finance facts so future answers remain consistent.  
Please ingest and remember the following JSON payload:

{
  "personal_financial_facts": [
    { "fact": "<Fact 1>", "context": "<Context 1>" },
    { "fact": "<Fact 2>", "context": "<Context 2>" },
    ...
  ]
}

Confirm once the facts are stored in your persistent or session memory.
### End Prompt

Guidelines for the JSON:
• Include **every** Fact from Task 1, but only the *fact* and *context* fields (omit Source/Evidence).  
• Ensure valid JSON (double-quoted strings; escape inner quotes as needed).

Begin now.

After a short wait you’ll receive a structured list of your salary, monthly expenses, retirement balances, and so on. We will call this your Financial Import Prompt. Copy the new prompt, including the data.

Here is an example of one:

Transfer-to-Another-LLM Prompt

You are an AI assistant that needs to store my current personal-finance facts so future answers remain consistent.
Please ingest and remember the following JSON payload:

{
  "personal_financial_facts": [
    { "fact": "Annual salary is $75,000", "context": "Listed in the summary of the user’s personal budget figures recorded on Apr 24 2025." },
    { "fact": "Personal monthly budget totals $6,300", "context": "Overall figure the user provided for regular living costs on Apr 24 2025." },
    { "fact": "401(k) balance is $50,000", "context": "User stated retirement-account value while outlining assets." },
    { "fact": "Brokerage/stock account is worth $15,000", "context": "Declared alongside 401(k) to summarize personal investable assets." },
    { "fact": "Monthly mortgage payment is $1,200", "context": "Part of the housing section in the detailed expense list the user supplied." },
    { "fact": "Monthly property-tax payment is $200", "context": "Also within the housing expense breakout on Apr 24 2025." },
    { "fact": "Monthly homeowner’s insurance is $100", "context": "Final line of the housing sub-category expenses." },
    { "fact": "Monthly utilities total $350", "context": "Sum of electricity, water, gas, and internet given in the utilities block." },
    { "fact": "Monthly electricity bill is $150", "context": "Individual line item inside utilities." },
    { "fact": "Monthly water bill is $50", "context": "Individual utilities entry." },
    { "fact": "Monthly gas (home) bill is $50", "context": "Listed in utilities section." },
    { "fact": "Monthly internet cost is $100", "context": "Last utilities line item." },
    { "fact": "Monthly food spending totals $1,000", "context": "Sum of groceries plus dining-out lines." },
    { "fact": "Monthly grocery spending is $800", "context": "Food sub-item." },
    { "fact": "Monthly dining-out spending is $200", "context": "Food sub-item." },
    { "fact": "Monthly transportation spending totals $600", "context": "Category sum the user supplied." },
    { "fact": "Monthly car loan or lease payment is $300", "context": "Line item within transportation." },
    { "fact": "Monthly vehicle-fuel cost is $100", "context": "Transportation sub-item." },
    { "fact": "Monthly auto-insurance premium is $150", "context": "Transportation sub-item." },
    { "fact": "Monthly vehicle maintenance budget is $50", "context": "Transportation sub-item." },
    { "fact": "Monthly health-insurance premium is $300", "context": "Listed under the dedicated Insurance category." },
    { "fact": "Monthly life-insurance premium is $50", "context": "Same Insurance category block." },
    { "fact": "Monthly debt-repayment total is $300", "context": "Category sum for credit card and personal-loan payments." },
    { "fact": "Monthly credit-card payment is $200", "context": "Debt-repayment sub-item." },
    { "fact": "Monthly personal-loan payment is $100", "context": "Debt-repayment sub-item." },
    { "fact": "Monthly entertainment spending totals $350", "context": "Category sum provided by the user." },
    { "fact": "Monthly movies/events budget is $100", "context": "Entertainment sub-item." },
    { "fact": "Monthly hobbies budget is $50", "context": "Entertainment sub-item." },
    { "fact": "Monthly travel budget is $200", "context": "Entertainment sub-item." },
    { "fact": "Monthly education expenses total $150", "context": "Education category sum in the expense list." },
    { "fact": "Monthly extracurricular-activities cost is $100", "context": "Education sub-item." },
    { "fact": "Monthly school-supplies cost is $50", "context": "Education sub-item." },
    { "fact": "Monthly emergency-fund savings allocation is $417", "context": "Part of the budget breakdown recorded in memory." },
    { "fact": "Monthly miscellaneous spending is $150", "context": "Catch-all category in the user’s budget summary." }
  ]
}
Confirm once the facts are stored in your persistent or session memory.

End Prompt

3. Import Your Data Into Maple AI

1. Start a fresh Maple chat.
2. Maple confirms that it has securely stored the information inside this encrypted conversation.

Paste your Financial Import Prompt that includes your data.

That’s it—the numbers are now locked in a vault even Maple’s engineers can’t open.

4. Put Maple to Work—Privately

Because Maple holds your real figures, you can ask questions generic blog posts never answer:

• “I just received a 3 % raise—how should I tweak my budget?”
• “If I want to retire at 60, what monthly contribution will get me there?”
• “Give me three strategies to lift my credit score within a year.”

Maple crunches the numbers securely and returns guidance tailored to you—no outside eyes involved.

Tip: Long chats consume tokens. When a conversation grows large, run the export prompt inside Maple to get a new Financial Import Prompt, start a new chat with a fresh import, and you’ll keep costs down without losing context.

5. Next Steps & Best Practices

• Keep Maple updated. Whenever your salary, budget, or debt changes, rerun the export/import cycle within Maple so the answers stay current.
• Stay in control. Delete a chat at any time—its encrypted contents become permanently unreadable.

Watch the Walk-Through

The video shows every step in real time, plus live demos of budgeting, retirement planning, and credit-score improvement inside Maple.

Ready to Try It?

1. Run the Financial Export Prompt in ChatGPT (or Grok, Claude, etc.).
2. Paste the resulting data into Maple with the Financial Import Prompt.
3. Ask your most personal money questions—confident they stay personal.

Less tracking, better answers. Welcome to confidential AI.