LLMs Are “Just” Coding Assistants — But That Still Changes Everything

Author(s): Towards AI Editorial Team

Originally published on Towards AI.

Understanding the Role of LLMs in Modern Coding: A Guide for Aspiring Developers

The rise of large language models (LLMs) has made AI development more accessible than ever. You can generate text, analyze data, and build AI-driven applications with just a few API calls. This accessibility has lowered the entry barrier, allowing anyone to create sophisticated products. However, moving beyond surface-level implementations to build scalable, production-ready AI solutions requires a solid foundation in programming.

Programming remains one of the most valuable skills for AI development. It’s not just for developers anymore — understanding programming helps anyone break down complex problems and build scalable solutions. Fortunately, learning to code is easier than ever, thanks to AI-powered tools that accelerate the learning process.

If you’re just starting your journey, you may wonder: where should you begin? Let’s break it down:

1. What to Learn — Choosing the right programming language.
2. How to Learn — Best approaches to mastering coding with AI tools.
3. How to Keep Learning — Staying up to date with AI developments.
4. Identifying What’s Relevant — Filtering noise and focusing on what matters.
5. How Much You Need to Know Before Applying — The truth about coding knowledge in today’s world.
6. How to Reduce Hassle to a Bare Minimum — Optimizing your learning process.

What to Learn

The first step is figuring out what to learn, so you have to choose what programming language to learn. Programming languages are the tools we use to communicate with computers. You may have heard of Python, Java, C++, or JavaScript. While they all serve the same fundamental purpose, each has unique strengths.

For beginners, Python is widely recommended. Its simplicity, readability, and extensive support in the AI community make it an ideal starting point. Python code often reads almost like English, reducing the initial learning curve.

For example, to display “Hello, World!” on the screen, you simply write: print(“Hello, World!”)

This one-line program shows the core coding process: you write an instruction, the interpreter executes it, and you see the result. Python’s simplicity makes it an excellent choice for learning AI development.

How to Learn

Traditionally, learning to code involved years of studying computer science theory before building real-world applications. LLMs have fundamentally changed this, enabling a more hands-on, project-driven learning experience.

With an LLM, you can:

• Generate code snippets instantly
• Ask for explanations of tricky concepts
• Get real-time debugging assistance

This shift has given rise to LLM-native development. We experimented with this in our Python Primer course, and early learners have responded incredibly well. In this top-down approach, you start with a project, use AI to generate initial code, and then explore how it works by asking follow-up questions. Instead of spending months learning abstract concepts, you gain practical experience from day one.

For example, if you want to build a task management system, the traditional learning path would involve:

1. Spending weeks studying Python syntax
2. Learning about databases and scheduling
3. Only then attempting to build the application

With LLM-assisted development, you can simply ask: “Write a Python script that stores tasks and sends a reminder for incomplete ones.” The LLM generates a functional code snippet, which you can tweak and refine by asking additional questions. This interactive learning process accelerates your understanding while keeping it engaging and practical.

Why Not Just Rely on LLMs for Everything?

If you can’t tell whether the LLM’s code is right or wrong, what would you do with it? LLMs have limitations. They sometimes generate incorrect code, misunderstand context, or produce inefficient solutions. That’s why developers should treat LLMs as coding assistants rather than replacements for fundamental programming knowledge.

Currently, LLMs are just coding assistants. That means you only need to know enough to spot their mistakes and iterate. This makes coding much easier, more efficient, and, honestly, more fun because you can create directly.

Think of an LLM as a junior developer: it can provide useful suggestions, but you still need to review, test, and understand the code. Additionally, coding isn’t just about syntax; it’s about problem-solving, designing efficient algorithms, and writing maintainable code. These skills develop through practice and experience.

How to Keep Learning

One challenge when learning Python specifically for LLMs is keeping up. LLMs evolve fast, so it’s easy to feel like your skills will be outdated within months.

But that’s not entirely true. Foundational skills don’t become obsolete. Yes, you need to upskill more frequently than before, but it has also become much easier to do so. We personally rely on the “LLMs as teachers” approach — asking LLMs to teach us. You no longer need endless Python courses. In our course, we consciously chose not just to teach Python but an LLM-native way of learning that allows you to keep teaching yourself.

One thing that helps? Asking questions about everything. Instead of memorizing syntax, focus on learning through projects. See a function? Ask the LLM what it does. Experiment with variations. The more you engage, the faster you learn.

Identifying What is Relevant

Staying up to date with AI developments is crucial, but not everything is relevant to your learning journey. The key is to filter out the noise and focus on what truly matters to your goals.

Our approach is simple:

1. Map out key resources to follow
2. Check those resources regularly
3. Find up-to-date solutions
4. Test things out (quick experiments go a long way)

At the same time, don’t overburden yourself with keeping up. Fundamentals go a long way.

How Much You Need To Know Before You Start Applying

In today’s world? Less than you think. The traditional path of spending years mastering programming before building something meaningful is outdated. Today, if you have a great product idea (or even a crappy one), start building.

With AI-assisted coding, you can experiment, iterate, and learn as you go. You don’t need to know every function or syntax rule beforehand. Instead, focus on problem-solving: break down your idea into steps, ask an LLM for help with implementation, and refine the solution based on feedback. This hands-on approach not only accelerates learning but also gives you real-world coding experience — exactly what matters when applying for jobs or launching your own project.

How to reduce hassle to a bare minimum

Would it be an oversell if we told you we’ve already considered all these steps and packed them into one course? Probably. But it’s true.

Python Primer for Generative AI is designed to give you just enough Python knowledge to talk effectively with an LLM and ask it to build or refine your code. As you interact with AI, you’ll start understanding how Python functions, data structures, and libraries work. You’ll also explore broader computing concepts like cloud services, APIs, and frameworks — everything needed to build complete applications.

Before you know it, you’ll be deploying a working web app that uses AI to automate tasks — without feeling like you had to slog through weeks of theoretical studies first.

The most exciting part? This course doesn’t just teach Python; it teaches you how to learn using LLMs. You’ll leverage AI as your personal tutor, learning to ask the right questions and get the best coding assistance — a game-changer for self-sufficient AI engineers.

What You Need to Remember

The key takeaway? You don’t need to master coding before you start building. With LLMs, learning to code is no longer a slow, theoretical process — it’s a hands-on, project-driven experience where you can learn by doing.

Yes, LLMs make coding easier. But the real advantage isn’t skipping the fundamentals — it’s accelerating your ability to think, create, and problem-solve.

So if you’re hesitating to start because you feel like you don’t know enough, don’t. Just dive in, build something, and let AI guide you along the way.

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Published via Towards AI