Introduction
For this project, I decided to extend my existing Budget Tracker application by adding a simple AI-powered budgeting assistant. The application already allowed users to create budgets and track their expenses, but it did not provide any guidance on how users could better manage their spending. By integrating AI, the application can now analyze a user's budget and expense information and generate practical budgeting suggestions.
The goal of this feature was not to replace the existing budgeting functionality but to enhance it by giving users quick insights that can help them make better financial decisions.
Why I Chose This Feature
I chose this feature because budgeting applications often focus only on recording transactions. While users can see where their money is going, they may not always know what those numbers actually mean or what changes they should make.
Adding an AI budgeting assistant makes the application more useful by providing personalized suggestions based on the user's financial data. Instead of simply displaying budgets and expenses, the application can now help users identify spending habits, recognize areas where they may be overspending, and encourage better financial planning.
This feature also fits naturally into the purpose of the application without changing the existing user experience.
Technical Decisions
For this implementation, I used the Vercel AI SDK together with Google's Gemini model. I selected Gemini because it provides a generous free tier, is easy to integrate, and produces fast responses suitable for short budgeting recommendations.
The frontend of the application was already built with React and Vite, while budget and expense data are stored in the browser's local storage. When the user requests AI advice, the application retrieves the stored budget and expense data, sends it to the AI model, and displays the generated recommendations within the application.
To protect the API key, it is stored in an environment file rather than being hardcoded into the application.
Integration Process
The first step was preparing the existing Budget Tracker project for AI integration by installing the required AI SDK packages. After obtaining a Google Gemini API key, it was stored securely using environment variables.
Next, I created a backend endpoint responsible for communicating with the AI model. This endpoint receives the user's budget and expense data, builds an appropriate prompt, sends it to Gemini, and returns the generated budgeting advice.
On the frontend, I added a "Get AI Budget Advice" button. When clicked, the application collects the user's current budgets and expenses from local storage and sends them to the backend. The returned recommendations are then displayed directly within the dashboard.
I also included basic error handling to ensure that users receive a helpful message if the AI service cannot generate a response.
Challenges Encountered
The main challenge was integrating AI into an application that was originally built as a frontend-only React project. Since AI models require API keys that should not be exposed to users, I needed to introduce a backend endpoint to securely communicate with the AI service.
Another challenge was designing prompts that produced short, relevant budgeting recommendations rather than long generic responses. This required refining the prompt so that the AI focused only on practical financial advice.
Reflection
If I were to continue improving this project, I would make the AI recommendations more personalized by considering spending patterns over time instead of only the current budgets and expenses. I would also improve the interface by adding loading indicators, better formatting for AI responses, and allowing users to ask follow-up budgeting questions.
Overall, integrating AI into the Budget Tracker demonstrated how artificial intelligence can add practical value to an existing web application with relatively small changes while improving the overall user experience.
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