Hidden Pitfalls of LLM in Education

We're living in a year where the phrase, "How do I add JavaScript to a Django admin panel?" brings up not a forum post or a StackOverflow answer but a near-perfect code snippet from LLM-powered assistants like GitHub's Copilot, Codeium, or ChatGPT in seconds. It feels like the future is here — at least for software engineers. These tools are becoming as common as keyboards and caffeine, and for good reason.

But — and there's always a "but" — what does this mean for the long-term growth of software engineers, especially those just starting their careers?

On one hand, these LLM (Large Language Model) assistants offer undeniable convenience. On the other, they introduce a whole new set of problems, some of which can go unnoticed until it's too late.

Imagine you're a junior developer, fresh out of a coding bootcamp or a CS degree. Your head is full of theory but your hands are not yet quite nimble with the code. You are a bit overwhelmed by the amount of information you need to absorb and the tools you need to know.

Along comes your shiny new AI-powered assistant. It's always there (almost always), never judges, ready to help with any issue and provide instant solutions to your problems.

Got a tricky bug?
LLM has got your back.
Need to implement a new feature you've never even heard of before?
LLM's already on it, providing you ideas and a ton of code.

What could possibly go wrong?

Well, a lot.

When LLMs spit out fully-formed code snippets, it might feel like you're flying, but beneath that soaring sensation, you're losing something vital — the struggle. And in software engineering, struggle isn't just part of the process — it is the process. Struggle is where the real learning happens.

In that struggle, you develop an understanding of the problem at hand, build a mental model of how systems work, and — most importantly — you make mistakes and learn from them. The instant gratification of LLMs short-circuits that process. Sure, they give you the answer, but you miss out on the why, the how, and often the crucial underlying context.

There's an old saying: Give a person a fish, and they'll eat for a day. Teach them to fish, and they'll eat for a lifetime. LLMs give you a boatload of fish right off the bat, but they don't necessarily teach you how to create your own net.

Here's the thing: the process of development isn't just about writing lines of code that work. It's about developing a mental model of how things fit together. It's about learning the intricacies of a programming language, understanding the thought processes behind a library's design, and ultimately, growing into an engineer who can solve problems independently and focus on delivering business value (that's actually what the Senior title is for).

The danger is that junior devs might use these tools like a band-aid, skipping the deeper learning process that's essential for long-term growth.

Here's an example: debugging. Any seasoned engineer will tell you that half of coding is figuring out why something doesn't work, not just getting it to work. Debugging builds your problem-solving skills, teaches you patience, and forces you to understand the intricacies of your system. When you skip that struggle with the help of an LLM, you may solve your problem in the short term, but you also lose out on the skills you'll need to debug more complex issues in the future. And let's be real — LLM-generated code isn't perfect. You will need those debugging skills at some point.

Another less obvious pitfall: LLMs don't know your codebase like you do (or should).

For instance, let's say you ask your LLM to generate a chunk of code that works with a certain library. The LLM might give you something functional, but does it follow your project's architecture? Does it align with your team's coding standards? And most importantly, will it scale and be easy to maintain down the line?

They're not psychic. LLM generates solutions in a vacuum, with no sense of the long-term implications. The result? They might give you a solution that looks fine on the surface, but under the hood, it could be a mess and a nightmare to maintain as your project evolves.

Maintenance is where things get hairy. LLMs are trained on vast datasets but often lack the context to know if what they're generating is maintainable in the long term. And when you're a junior developer, you're even less equipped to spot these problems. You happily integrate that slick-looking solution, only to find yourself — or worse, your teammates — spending hours untangling a convoluted mess months down the road.

There's a lot of value in taking the slow road — especially when you're starting out. Spending an hour poring over documentation isn't just about finding the answer to your current problem; it's about investing in your future self. It's about building a foundation of knowledge that you can draw on for years to come.

The problem with LLMs is that they encourage output over understanding. Sure, you get a solution fast, but at what cost? You end up relying on the tool for basic knowledge that you really should be learning on your own. The more you lean on the LLM, the less you learn, and the less independent you become as an engineer.

On the other hand, spending that same hour trying to fix an LLM-generated solution that doesn't quite work is often just a waste of time. You end up with a solution, sure, but what have you learned? Probably not much beyond "LLM doesn't always get it right".

In fact, this phenomenon mirrors the broader trend we've seen in the AI world, especially when you consider the development of large-scale models like GPT-4, GPT-5, and beyond. These systems — originally birthed from the open-source community — are becoming more closed off. Major players like Google, OpenAI, and Microsoft hold the keys, hoarding the real power of AI behind closed doors, keeping us all a little too dependent on their systems.

AI's role in development is only going to grow. The question is, will AI tools like LLMs empower small teams and individual developers, or will they remain the domain of tech giants? The accessibility of these models is the million-dollar question.

At the moment, the future seems divided. On the one hand, cloud services and affordable GPUs have made it easier than ever for small teams to fine-tune models, but as these systems grow in complexity, it might become infeasible for the average developer to truly own these tools.

Much like search engines before them, the inner workings of these LLMs are becoming increasingly opaque. Take ChatGPT, for example. OpenAI started with open-source roots but has now restricted access to the details of its models. This could lead to a future where LLMs are used as opaque black boxes — tools that get you the answer but don't let you peek under the hood.

It's like handing someone a car but never teaching them how the engine works. At some point, you have to ask yourself: Is this empowering, or is this just a hidden dependency?

So, where does that leave us? Are LLMs a threat to your career growth, or are they a valuable tool in the right hands? I'd argue it's all about balance.

Here are a few tips for using LLMs without letting them stunt your learning:

• Leverage LLMs for repetitive tasks: Need to generate boilerplate code, unit tests, or mock data? Perfect. This is where LLMs shine. Let them do the grunt work so you can focus on more complex problems.
• Never skip the documentation: Even if LLMs give you a ready-made solution, take the time to read the docs. You need to understand the context of the libraries and frameworks you're using.
• Always refactor LLM-generated code: Don't take their output as gospel. Review it, refactor it, and ensure it adheres to your project's architecture and coding standards.
• Pair LLMs with your learning: Treat them as a supplement, not a replacement. When an LLM gives you a solution, dig into it. Ask yourself why it works. Learn from it.

The rise of LLMs is undoubtedly exciting, and their potential to revolutionize the way we code is real. But for junior developers — and even seasoned ones — there's a fine line between using these tools effectively and letting them stunt your growth. Don't be lulled into complacency.

So, yes, the future has arrived, and it's packed with LLM-powered goodies. But don't let that distract you from the fundamentals. Read the documentation, ask questions, and most importantly, keep learning. Because that's the path that will take you from being just another coder to becoming a true software engineer. LLMs are just a tool. It's your job to ensure they stay that way.