AI in Backend Development: A Cautionary Tale for North East India

In the rapidly evolving world of technology, Artificial Intelligence (AI) has been hailed as a game-changer. Its potential to automate and streamline various tasks, including backend development, has sparked excitement among developers worldwide. However, a recent experiment serves as a reminder that AI, while powerful, is not infallible and requires careful handling.

Assumptions and Misunderstandings

One of the key lessons from the experiment was that AI struggles with understanding assumptions. For instance, when asked to generate an endpoint for sending notifications, the AI assumed that network calls would always recover and rate limits were a suggestion. This oversight could lead to unintended consequences, such as infinite retries and potential overloading of external systems.

Security Vulnerabilities

Another concern that emerged was the potential for security vulnerabilities. AI was found to expose internal IDs, log sensitive payloads, and skip edge-case authentication checks. These issues were not immediately apparent as they did not cause crashes or errors, making them all the more insidious.

Business Logic Nuances

AI also demonstrated a tendency to focus on patterns rather than intentions. When asked to validate a user's eligibility for an action, the AI missed the reasons behind certain conditions, leading to incomplete validation. This underscores the importance of human oversight in complex business logic scenarios.

A Surprising Win

Despite the challenges, AI proved to be an asset in certain areas. It excelled at generating boilerplate code, DTOs, mappers, and test case scaffolding. Moreover, it showed a knack for explaining the original code back to the developer, making it easier to understand complex logic.

Lessons Learned and Future Steps

The experiment underscored the importance of using AI judiciously. AI should be used for skeletons, repetitive logic, tests, and documentation, but not for critical areas like authentication, retries, and money-handling. In the future, developers should use more explicit prompts and rigorous testing to mitigate potential issues.

AI: A Brilliant Intern, Not Your Tech Lead

In conclusion, while AI did not break the backend, the overconfidence in its capabilities did. However, the experiment was not a complete failure. It served as a valuable learning experience and reinforced the need for human oversight in the AI-driven development process. As we continue to harness the power of AI, it is essential to approach it with caution and a healthy dose of skepticism.