The way we build software is evolving rapidly, with AI driving the change. Imagine you’re working on a finance project, and all you have to do is open your IDE, type a simple prompt like, “Create a dashboard for tracking monthly expenses,” and AI generates everything—code, tests, and even documentation. This isn’t just a vision for the future; we’re already moving in that direction.

With AI getting smarter every day and processors becoming more powerful, the idea of AI handling most of the heavy lifting in coding isn’t far off. In this article, I’ll share what I imagine the future of coding with AI could look like, especially in the context of building something like a finance application. Let’s dive in!

Future Possibilities

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  1. AI as a True Coding Assistant

AI will become a real-time coding partner embedded in IDEs. It will not just autocomplete code but also handle database changes, schema updates, and backend/frontend integration effortlessly.

Example:

Say you’re adding a feature to your finance app to calculate interest rates. You could simply prompt the AI:

Add a repayment status column to the funding table and integrate it into the backend API and front-end.

  • Liquibase ChangeSet for Database Update: AI creates a Liquibase changeset to add a repayment_status column to the funding table.
  • Java Backend Update: AI updates the FundingEntity and FundingService to include the new column. AI also updates the API to allow fetching and updating the repayment status.
  • ReactJS Frontend Update: AI generates a Repayment Status Dropdown in the funding page.
  • Unit Test Generation: AI generates unit tests for the FundingService to ensure the repayment status updates correctly.
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  1. Intelligent Debugging and Issue Resolution 

Debugging has always been a time-consuming part of software development, but with AI, this could become a breeze. Imagine running your application and encountering a runtime error. Instead of scouring logs and Googling error messages, you’d have an AI assistant that immediately analyzes the issue, explains the root cause in plain language, and suggests a fix.

For instance, say you deploy your finance app, and users report that the “Calculate Interest” feature is returning incorrect values. Here’s how AI might help:

  • Error Analysis: AI identifies that the issue lies in the interest rate calculation logic due to an edge case where the principal amount is zero.
  • Proposed Fix: It suggests modifying the formula to handle zero-principal cases and provides an updated code snippet.
  • Automated Test Creation: It generates a unit test to cover the edge case and ensures the fix doesn’t break existing functionality.

This type of AI assistance would allow developers to focus on solving complex problems instead of wasting time on mundane debugging tasks.

  1. AI-Driven System Design

In the future, designing systems from scratch will be as simple as sketching an idea on a whiteboard. Imagine describing your finance app to the AI:

“I need a system where sellers can upload invoices, funders can finance them, and admins can manage everything.”

Within minutes, AI could generate:

  • ERD (Entity-Relationship Diagram): A detailed schema showing how tables like Sellers, Invoices, and Funders relate to one another.
  • Microservice Architecture: A blueprint for splitting your app into services, such as authentication, invoice management, and reporting.
  • DevOps Setup: Automated CI/CD pipelines and infrastructure provisioning using tools like Docker and Kubernetes.

What used to take days or even weeks could be achieved in a fraction of the time, letting developers focus more on refining business logic and user experience.

  1. Customizable AI Models

The future will bring AI models tailored to specific industries. Imagine you’re building a finance app, and instead of using generic AI, you leverage an AI fine-tuned for financial systems. This specialized AI would understand industry jargon, regulatory requirements, and even common patterns in finance workflows.

For example, you could ask it to:

  • “Generate a report showing overdue invoices grouped by buyer.”
  • “Ensure compliance with GDPR when storing user data.”
  • “Optimize database queries for retrieving large datasets.”

The AI would not only generate accurate results but also explain its reasoning and suggest best practices.

  1. Collaborative AI in Teams

In the same way tools like Git allow teams to collaborate on code, AI will enable even deeper collaboration. Picture an AI that understands your entire codebase and helps align team efforts. It could:

  • Suggest Task Assignments: Based on each team member’s strengths, the AI could recommend who should handle a specific feature or bug.
  • Ensure Code Consistency: It could review pull requests to enforce coding standards and detect potential merge conflicts.
  • Provide Onboarding Assistance: For new developers, the AI could act as a mentor, explaining the architecture and suggesting where to start.

This would make team workflows smoother and help bridge skill gaps.

Final Thoughts

The future of coding with AI promises to be exciting and transformative. It won’t replace developers but will augment our abilities, enabling us to build complex systems faster and more efficiently. As AI continues to evolve, the line between what’s possible today and what we can only imagine will blur, pushing the boundaries of how we create software.

So, let’s embrace this journey and see where it takes us. Who knows? The next big innovation might just be a prompt away.