I Don't Understand How People Use ChatGPT to Write Production Code – R/ChatGPT
Right there with you. I mean, I get it. ChatGPT is powerful. But when I see people confidently dropping AI-generated code into production, I raise an eyebrow. And honestly, I was pretty skeptical at first. How could a language model truly understand the nuances of a complex project, the subtle gotchas, and the long-term implications?
But over time, I've come around to its use as a powerful tool, and I'm gonna share a few things I've learned the hard way about working with AI in a code-first world. Let's dive in:
Where ChatGPT Really Shines:
1. Rapid Prototyping: This is its sweet spot, in my opinion. When I'm blocked on a new idea, I dump the first few lines of code in, and ChatGPT can quickly give me some ideas, and boilerplate code.
2. Code Explanations and Debugging: If I'm struggling with a section of code, ChatGPT can help explain it line by line, which comes in handy, especially with libraries I'm less familiar with. It's saved me hours!
3. Code Generation with Specific Constraints: ChatGPT can write code with specific constraints if you train it properly. Start with a specific codebase and tell it to follow design patterns. Its amazing, actually!
4. Refactoring and Optimization: I've found that ChatGPT can be surprisingly good at suggesting improvements to existing code and pointing out potential performance bottlenecks. Again, use with caution. It won't catch everything.
5. Generating Unit Tests: Writing unit tests can be tedious, and ChatGPT is great at taking a function and generating appropriate test cases to show how it works.
What To Watch Out For:
1. Hallucinations: ChatGPT can confidently fabricate code. Always, always, always double-check the output, especially if you're not intimately familiar with the libraries it's using.
2. Security Vulnerabilities: ChatGPT might generate code with known security flaws, so you need to be extremely vigilant about that.
3. Contextual Errors: ChatGPT sometimes struggles to maintain context across multiple prompts, leading to inconsistencies or errors. It's easy to miss.
4. Lack of Understanding: ChatGPT doesn't truly “understand” code. It's pattern-matching, so if your code has a unique approach or is heavily reliant on domain-specific knowledge, the quality can drop quickly.
These are just my experiences!
In short, treat it like an extremely helpful intern. Don't blindly trust the output, be prepared to debug extensively, and always maintain a healthy dose of skepticism. The right balance: ChatGPT lets you code faster; your brain ensures you keep writing better code.
And, full disclosure: because I'm constantly working with ChatGPT, I was tired of re-uploading files, re-explaining my projects, and generally starting from scratch every single time in those chats. I stumbled upon Contextch.at a while back. It's basically a way to set up "projects" with all your context and data, and spin up new chats that already know everything. It's saved me a ton of time – I can just start a chat whenever I need a quick code assist. It has saved me a bunch of headaches.