# I Have a Problem that Can be Solved by AI: Financial Assistant

**From problem identification to implementation plan.**

We've covered the theory of problem identification and LLM capabilities. Now let's see how this works in practice with a complete example: building a Personal Finance Assistant.

This isn't just an example - it's the actual thinking process you need for any AI application. We'll work through the messy reality of taking a vague idea and turning it into something specific you can build and deploy.

## How We'll Work Through This

We'll break this case study into three parts that cover the main decisions you need to make:

**Problem Identification** - Start with the messy reality of "people are bad with money" and figure out what specific problem we can actually solve. This means talking to real people, understanding their current solutions, and validating that our idea makes sense.

**Technical Design** - Figure out whether AI actually helps with this problem or if we're just adding complexity for no reason. Design a system that uses AI where it's useful and traditional programming everywhere else.

**Implementation Plan** - Decide what to build first and how to know if it's working. Most projects fail because they try to build everything at once instead of proving the core idea first.

<div class="grid cards" markdown>

-   :material-magnify:{ .lg .middle } **[Problem Identification](problem-identification.md)**

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    From "people are bad with money" to a specific problem we can solve

-   :material-cog:{ .lg .middle } **[Technical Design](technical-design.md)**

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    When AI helps and when it doesn't, plus system architecture

-   :material-rocket-launch:{ .lg .middle } **[Implementation Plan](implementation-plan.md)**

    ---
    
    What to build first and how to measure success

</div>

## What You'll Get From This

Most AI tutorials show you how to call APIs or fine-tune models. This case study shows you how to think through whether you should build something in the first place.

We'll work through the actual decisions: Is this problem real? Do people care enough to pay for a solution? Will AI actually help or just add complexity? What should you build first?

The personal finance example is useful because it's a problem most people understand, but the thinking process works for any domain. Whether you're building healthcare tools, business applications, or consumer apps, the questions are the same even if the answers are different.

[Start with Problem Identification →](problem-identification.md){ .md-button .md-button--primary }