Harnessing AI for Software Engineering: How I Built FundSage 1.0.0 with ChatGPT-4o as My AI Development Team

I'm thrilled to announce the completion and release of FundSage 1.0.0! This milestone marks the culmination of a unique software development journey where I, as the Lead Software Engineer, orchestrated a team of AI-powered collaborators—ChatGPT 4o —to help transform an initial concept into a fully functional release in just 10 days.

This is the first part of a blog series where I’ll delve into the prompt engineering techniques I used to guide ChatGPT to perform as a Coder, Test Engineer, and Technical Copy-Writer. Through this series, I aim to share insights on harnessing Generative AI as development partners, showcasing how innovative AI-human collaboration can significantly accelerate software projects.

?? FundSage

TL;DR: FundSage is a showcase project crafted to highlight best practices in software engineering within a simulated Financial Assistance Scheme Management System. ?? This project demonstrates the application of SOLID design principles, robust testing strategies, and streamlined deployment processes. Dive into our documentation to set up the project on your local machine or to explore the architecture and design decisions in depth! ??

GitHub Repository: jawkh/fund-sage (github.com)

?? Overview

FundSage is a fictitious system designed to manage various financial assistance schemes, including their eligibility criteria, application processes, and benefit distribution. This project showcases clean, maintainable, and scalable software engineering practices, making it an invaluable learning resource for both novice and experienced developers. ??

?? Key Features

• ?? Adherence to Software Engineering Principles: The codebase strictly follows best practices like SOLID principles, well-architectured enterprise solution design, proper implementation of RESTful principles and good coding and extensive testing practices to ensure clean and effective code that has good quality, readabilty, maintainability and extensibility.
• ?? Extensibility with SOLID Design Principles: FundSage is built with SOLID principles at its core, allowing for easy extensibility and maintenance

• ??? ORM for Data Persistence: Utilizes SQLAlchemy for efficient data management, clearly separating business logic from data access layers.
• ?? Database Synchronization: Automatically handles database schema changes with migration scripts, keeping your data model in sync.
• ? Quality Assurance with Pytest: Implements a comprehensive testing strategy using Pytest to cover unit, integration, and end-to-end tests, ensuring code reliability and robustness.
• ?? Container Packaging: Uses Docker for containerization to streamline deployment, encapsulating the entire application environment for consistent development and production stages.

?? The Journey from Concept to Completion

The inception of FundSage began with a vision to create a streamlined platform for managing financial assistance schemes. The ambitious timeline of 10 days (more like 10 late-nights) meant juggling rapid development alongside my hectic full-time job. This was no ordinary coding sprint; it was a test of precision, efficiency, and effective communication—both human and AI.

My Role as the Human Lead

I wore multiple hats: System Designer, Prompt Engineer, and Integration Engineer. By combining human ingenuity in software design and AI capabilities, we achieved a sophisticated architecture that met complex requirements while being scalable for future enhancements. Every step of the way, I acted as the orchestrator, using prompts to direct ChatGPT in various technical roles.

ChatGPT as the Development Team

ChatGPT was meticulously instructed to perform specialized roles, forming a unique "team":

• ChatGPT as Coder: Implemented my system designs by generating precise code for key components such as API endpoints and authentication flows.
• ChatGPT as Test Engineer: Generated exhaustive automated test cases, identified edge scenarios, and validated outputs to ensure robustness.
• ChatGPT as Technical Copy-Writer: Generated clear and concise documentation, making the platform accessible for future developers and users alike.

?? Overcoming Challenges

Bringing FundSage 1.0.0 to life in 10 days was no small feat. Here’s a look at the major challenges we tackled and how AI-human collaboration made a difference:

Design and Architecture

I led the high-level architecture design, ensuring we adhered to best software engineering practices like modularity and scalability and incorporated the SOLID design patterns. Prompt engineering allowed ChatGPT-4o to assist with coding detailed functions while adhering to my design principles.

API Development

ChatGPT handled much of the initial code generation for our RESTful APIs. I iteratively refined the AI’s output, ensuring all endpoints were secure, efficient, and aligned with our data validation requirements.

Testing and Validation

The AI "Test Engineer" was vital for creating automated tests and validating code against edge cases. It significantly sped up the QA process, allowing me to maintain the project’s rapid pace without compromising on quality.

Documentation and Usability

The "Technical Copy-Writer" role allowed the AI to generate documentation in parallel to code development, resulting in API references and integration guides that were comprehensive and developer-friendly.

Results: AI-Driven Efficiency and Quality

Leveraging AI in this way allowed us to achieve several benefits:

• Rapid Development Cycles: We quickly iterated on new features, balancing speed with precision.
• Automated Testing: The AI handled repetitive regression tests, providing strong validation with every code refactor.
• Efficient Documentation: The AI-generated documentation shortened the time required for manual drafting and ensured consistency across all reference materials.

?? A Synergy of Human and AI

FundSage 1.0.0 is more than just a product release; it’s a case study in the effectiveness of man-machine collaboration. Together, we’ve set a new standard for what can be accomplished by leveraging AI models as true collaborators in software development.

?? Looking Ahead

This release is just the beginning! In the next parts of this blog series, I’ll be sharing my prompt engineering techniques, offering a deep dive into how I tailored prompts to maximize each AI role’s capabilities, refined responses for complex technical tasks, and managed AI collaboration effectively.

Here’s to the next evolution of AI-driven software engineering! ??