When ChatGPT Becomes Your Coding Partner: A Real-World Java Framework Migration Saga

For years, I resisted using Large Language Models for serious development work. That changed when I faced an urgent task: migrating intricate Spring Boot functionality to Quarkus and Micronaut frameworks—with zero prior experience in either. With colleagues on vacation and deadlines looming, I turned to ChatGPT as an emergency pair programmer. What followed was equal parts revelation and cautionary tale.

The Quarkus Quandary: Helpful Starts and Compiler Lies

My initial Quarkus challenge involved mimicking Spring's runtime aspect-oriented patterns in a compile-time constrained environment. ChatGPT quickly generated seemingly viable @Interceptor and @AroundInvoke implementations—an impressive starting point when documentation fell short. But the code failed compilation, exposing ChatGPT's dangerous confidence in incorrect Jakarta EE specifications:

"ChatGPT insisted interceptors didn't require specific method annotations. The compiler violently disagreed," I discovered after consulting actual CDI documentation.

The assistant then entered a maddening loop: proposing alternative flawed approaches, acknowledging constraints when challenged, then circling back to previously invalid solutions. This pattern revealed a critical weakness: LLMs struggle with contextual memory during complex, multi-turn problem-solving.

Micronaut Misadventures: Hallucinations in Production Garb

The next day brought Micronaut migrations—a framework entirely new to me. Here, ChatGPT's proposals grew more unmoored. It hallucinated non-existent interfaces and suggested methods that simply didn't belong in Micronaut's architecture.

"Is this the hallucination part?" I wondered after receiving blatantly wrong implementation suggestions.

What fascinated me most wasn't the error, but the correction dynamic: when challenged, ChatGPT would apologize and offer working (though not necessarily correct) alternatives. This raises unsettling questions: Why didn't it lead with the verifiable solution? Where does this 'second layer' of knowledge emerge from?

The Pragmatic Programmer's AI Toolkit

Despite frustrations, the experience highlighted genuine utility:

• Accelerated Onboarding: For niche framework tasks lacking Stack Overflow coverage, ChatGPT provided crucial starting points faster than documentation trawling.
• Concept Clarification: It excelled at explaining underlying mechanisms (e.g., compile-time vs runtime DI) when framed as learning queries rather than solution demands.
• Solo-Dev Lifeline: As developer Manda Putra observed:

"Use it to learn new tools... it resulted in a better understanding of how the library works!"

Yet critical caveats emerged:

1. Compilation ≠ Correctness: Code that builds may still violate framework paradigms
2. Constraint Amnesia: LLMs frequently "forget" previously stated requirements
3. Expertise Amplifier, Not Replacement: Output quality inversely correlates with problem novelty

The Verdict: Skeptical Co-Pilot, Not Autopilot

This experiment hasn't made me an LLM evangelist. ChatGPT proved most valuable as a conversational documentation accelerator for well-trodden paths, but dangerously unreliable for framework-specific innovation. Its greatest sin isn't inaccuracy—it's presenting hallucinations with unwavering confidence.

For now, I'll keep AI in my toolbox as a last-resort brainstorming partner when human collaborators vanish. But like any junior developer, its output demands ruthless verification. The true cost of AI-assisted development isn't the subscription fee—it's the vigilance required to catch its elegant, convincing mistakes.