Using Reasoning Models for Personal Financial Planning

I Fed My Midlife Crisis Into A Reasoning Model

TLDR: Standard LLMs hallucinate numbers and make up financial advice, but reasoning models are logic engines that can handle sequential math, complex constraints, and stress-testing. For personal financial planning, the difference is critical—one gives you a Ponzi scheme, the other gives you an actual plan.

Summary:

There's a distinction being drawn here that matters for anyone using AI for consequential decisions: the difference between text predictors and logic engines. When you ask a standard chatbot to "plan my retirement," you get confidently wrong answers—hallucinated 12% annual returns and advice that sounds reasonable but doesn't survive contact with math. Reasoning models (like OpenAI's o1 series) are fundamentally different. They "think" before they speak, handling sequential calculations and complex constraints without fabricating numbers.

The context is familiar to anyone in tech right now. Layoffs sweeping through major companies, colleagues suddenly "open to work" after fifteen-year tenures, and a general economic anxiety humming in the background. The author's starting point—dusty 401ks, an unopened Robinhood account, a spreadsheet from 2023—is uncomfortably relatable. Most of us have financial blind spots we'd rather not examine.

The approach of feeding an entire (anonymized) financial life into a reasoning model and treating it as "therapy for your bank account" is an interesting use case. The key insight is that reasoning models can do what standard LLMs cannot: handle the math correctly while simultaneously understanding the nuance of personal constraints. This isn't about asking for generic advice—it's about stress-testing your actual numbers against realistic scenarios.

For architects and teams thinking about AI application design, this illustrates an important pattern: matching model capabilities to task requirements. Reasoning models excel at multi-step logic problems with quantitative precision requirements. Using a standard chatbot for financial planning is a category error—you're using a tool optimized for text generation on a problem that requires mathematical reasoning. The same principle applies across domains: choose your model based on what the task actually demands, not on what's most convenient.

The 8-step workflow approach also demonstrates good prompt engineering practice: breaking complex problems into structured, sequential steps rather than asking for everything at once. This reduces hallucination risk and makes it easier to verify each component of the output.

Key takeaways:

• Reasoning models are logic engines that think before responding, unlike text-predictor chatbots
• Standard LLMs will hallucinate financial numbers—use them for brainstorming, not calculations
• Complex personal planning benefits from structured multi-step prompts, not single requests
• Stress-testing financial plans against adversarial scenarios reveals assumptions that break under pressure
• The right tool for financial planning is one that can handle sequential math with constraints

Tradeoffs:

• Reasoning models provide accuracy but require more compute time and cost per query
• Structured multi-step workflows produce reliable outputs but demand more upfront prompt engineering effort

Link: I Fed My Midlife Crisis Into A Reasoning Model

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