Copy, Paste, Repeat: My Frustrations Coding with ChatGPT
Writing code is like constructing a complex, multifaceted puzzle. It involves a delicate balance of knowledge, intuition, context awareness, and a constant pursuit of evolution. The arrival of AI-based coding assistants such as ChatGPT brings an exciting prospect. But like all technological advancements, they come with frustrations and limitations that can sometimes overshadow their appeal.
The Frustration of Context
Imagine working with web frameworks like Django, where following the Model-View-Controller (MVC) pattern is the norm. You’re constructing an API endpoint deeply intertwined with various components like models, views, controllers, and sometimes even specialized service functions or classes. This is where the first main frustration with ChatGPT surfaces: the lack of context of the whole code base.
With ChatGPT, you’re essentially working with an intelligent but isolated entity. It's akin to trying to compose a symphony with a musician who can only play one instrument and has no idea what the rest of the orchestra is doing. Since ChatGPT lacks access to all the underlying connections in your project, it often generates incompatible or outright wrong code. You find yourself caught in a loop, explaining the structure, correcting the generated code, and fighting against the tool meant to assist you.
Being Out of Sync with Time
In the ever-evolving world of technology, being up-to-date is not a luxury; it's a necessity. The irony of using ChatGPT to build AI apps is an illustrative case of this frustration. Popular LLM libraries like LangChain and Llama Index were released after ChatGPT's knowledge cut-off.
Imagine the excitement of working with these libraries, only to find that your trusty AI assistant knows nothing about them. You find yourself facing the stark limitations of ChatGPT, a tool otherwise rich in knowledge but now trapped in time.
You could use the browsing feature, hoping it would bridge the knowledge gap, but it's like trying to tune into a live concert through a static-filled radio. It's slow, frequently fails to understand the parsed documentation, and the entire process becomes a trial in patience.
Copy, Paste, Repeat
The inability of ChatGPT to access your local file system brings us to the next layer of inconvenience. This tool, which could be a seamless extension of your coding workflow, cannot create or edit files directly. Instead, it forces you into a monotonous cycle of copying and pasting between ChatGPT and your editor.
What initially feels like a minor nuisance soon morphs into a friction-laden process that disrupts your flow. Every switch from your IDE to ChatGPT becomes a jarring experience, interrupting your train of thought, breaking the rhythm of creativity, and turning a promising innovation into a chore.
The Illusion of Debugging
Debugging is an art and a science, requiring a deep understanding of the code, its variables, and how they change over time. ChatGPT might help you fix your code, but it doesn't truly debug it. The absence of real-time interaction with the code's execution makes ChatGPT's assistance superficial at best. It's like having a co-pilot who can read the map but cannot feel the turbulence or adjust the controls when needed.
Conclusion
ChatGPT brings a fascinating blend of potential and pitfalls. It promises to be a companion in the coding journey but often falls short in understanding the complex tapestry of a real-world project. Its inability to adapt to changes, engage with the context, and truly become a part of the coding ecosystem reveals that we are still at the beginning of this technological exploration.
But, like all beginnings, it is filled with lessons, frustrations, and the promise of what lies ahead. The challenges with ChatGPT are not merely obstacles but signposts pointing toward the future, reminding us that innovation is a journey, not a destination.