The debate about using AI for learning programming is alive and well. Some argue that relying on AI can stunt a developer's growth, while others see it as a productivity game-changer. I fall into the second camp: AI is a powerful tool that, if used correctly, can redefine what is essential for a modern programmer. Just as calculators replaced the abacus, AI is shifting the landscape of coding skills. Instead of clinging to outdated methods, it's more practical to adapt. In this guide, I'll show you how I would approach learning programming today, blending the benefits of AI with a solid foundation of core knowledge.
Why Some Skills Will Become Obsolete
Technology has a knack for making certain skills redundant. Many of us no longer perform long-form arithmetic or manually read analogue clocks because we have digital tools that do it faster and more reliably. In the software world, repetitive tasks, once the bulk of junior-level work, are automated or handled by AI. Tools like GitHub Copilot, which integrates Claude (the best LLM for coding) as an advanced AI engine, or ChatGPT (good and free), are perfect examples of how automation is evolving.
- Outdated tasks: Basic CRUD operations, creating boilerplate code, and repetitive debugging can now be done faster with AI.
- Enduring skills: Creativity, problem-solving, collaboration, and the ability to reason about code will never go out of style.
The argument of "What if there's no electricity?" or "What if the tools fail?" is less compelling in the modern era. We rely on technology in virtually every facet of life, and programming is no exception. It's more important to stay adaptable and learn skills that can't be easily replaced.
The AI Paradox: Beginners and Experts
In my experience, I've seen junior developers outpace their more senior colleagues simply because they were better at prompting and collaborating with AI. At the same time, I've also encountered novices who became helpless without AI suggestions. This leads to the AI paradox:
- AI as an equalizer: It can make novices productive quickly.
- AI as a crutch: It can also leave you vulnerable if you never learn how to solve problems independently.
The solution? Strike a balance. It's not about rejecting AI or depending on it entirely; it's about leveraging AI's strengths while cultivating your expertise.
Step 1: Learn the Basics Without AI
Your first milestone is to understand programming fundamentals the traditional way. This means:
- Syntax and Data Structures: Familiarize yourself with variables, loops, conditionals, lists, and dictionaries.
- Basic Algorithms: Practice sorting, searching, and algorithmic thinking.
- Debugging and Error Handling: Learn to read error messages and trace problems logically.
During this phase, avoid the temptation to ask AI for direct answers. Instead, build your foundation using textbooks, online tutorials, or courses. By understanding the "why" behind each concept, you'll be better equipped to interpret AI-generated code later.
Step 2: Improve Your Prompting Skills
Once you have the basics down, bringing AI into the picture is time. Prompting effectively can spell the difference between a helpful answer and a useless one. Here are some tips:
- Be Specific: Provide as much context as possible. Instead of "How do I fix this bug?" say, "I am trying to do X, but I'm getting a TypeError when I call function X with parameter Y."
- Iterate: If the answer isn't what you need, revise your question. Consider how you might ask a human colleague for clarity and apply that same logic. Learn and improve the clarity of your communication.
- Ask Follow-ups: Dig deeper. If you receive a partial answer, keep iterating until it's crystal clear.
Practising these skills will improve your AI interactions and enhance overall communication, which is useful when collaborating with real teammates.
Step 3: Use AI to Accelerate Learning, Not as a Crutch
AI can speed up your workflow significantly by generating boilerplate code, writing test cases, and even suggesting optimizations. The trick is to rely on AI for efficiency, not expertise. For instance:
- Generate Sample Code: Ask AI for quick examples of implementing a pattern or function.
- Refactor: Have AI refactor your code to follow best practices, then study what changed.
- Explore Alternatives: If you're stuck, ask AI for multiple approaches to a problem, compare them, and choose the best one.
Always validate the AI's output by running tests, reading documentation, and applying your understanding. Over time, this helps you recognize good solutions from bad.
Step 4: Choose Challenging Technologies Enhanced by AI
One of the best perks of learning with AI is that it reduces the intimidation and learning curve factor of complex technologies. For example, Django is the best Python web framework for productivity, but the learning curve once seemed daunting; now, AI can guide you through the setup and code generation. GitHub Copilot can offer in-editor suggestions based on the code you're writing, while Claude and ChatGPT provide additional explanations and examples on demand.
To maximize your learning:
- Select technologies that have strong community and documentation support. This way, you can rely on official docs and AI assistance.
- Aim for tools with long-term relevance. Frameworks like React, Django, or Node.js have large ecosystems, which means abundant resources and AI references.
- Experiment boldly. Don't shy away from advanced features; ask AI to break them down or give practical examples.
The result? You'll become comfortable with the framework's inner workings much faster than if you were alone.
Step 5: Build Real Projects
Nothing beats hands-on experience. Choose projects that challenge you to apply new concepts and step outside your comfort zone:
- Personal Portfolio Sites: Showcase your skills while experimenting with new libraries.
- Data Analysis Projects: If you're into data, build projects around APIs, data wrangling, or visualization.
- Open Source Contributions: Contribute to a project on GitHub. AI can help you navigate the codebase, but you'll still need to do the heavy lifting of understanding project requirements and reviewing your changes.
Remember, AI can generate code, but you're responsible for testing, refining, and ensuring the final product works as intended. This process cements your learning in a way that tutorials alone never will.
Step 6: Master Reading Documentation
AI, while powerful, can sometimes produce incorrect or outdated information. Knowing how to read official documentation is non-negotiable in software development. It not only saves time but also helps you validate AI's suggestions. Here's how to make the most of it:
- Start with the Basics: Skim the introductory sections of the documentation to get a solid overview.
- Follow Official Guides: Many platforms provide step-by-step guides and “getting started” tutorials.
- Dive Deeper: Explore advanced topics and keep the docs bookmarked or open in a separate tab.
By cross-referencing AI-generated code with documentation, you'll catch errors and learn the correct usage of functions or libraries.
Will AI Replace Programmers?
The short answer. Not entirely.
AI is transforming the industry by automating monotonous tasks like boilerplate code and simple bug fixes. As a result, the bar for what constitutes "junior-level" work is rising, and senior developers are now expected to deliver 10x output by leveraging AI.
- New Baseline: Junior developers must quickly learn advanced patterns and practices, skills AI can accelerate but not replace.
- Evolving Roles: Senior developers are expected to strategize, mentor, and build complex systems. AI helps with speed, but you still need deep knowledge to oversee architecture and quality.
- The Human Factor: Creativity, critical thinking, communication, and domain expertise remain uniquely human strengths.
AI will continue to grow in importance, but it won't eliminate the need for skilled programmers; it will simply elevate the expectations placed upon them.
Conclusion
If I were learning programming today, I would fully embrace AI while ensuring I have a solid grasp of the fundamentals. Start by learning core concepts without shortcuts. Then, bring AI into your workflow to handle mundane tasks and provide quick examples, but always maintain an active role in decision-making and critical thinking.
Ultimately, the best programmers combine mastery of the fundamentals with the strategic use of AI. Focus on problem-solving, collaboration, and continuous learning. AI is here to stay, and the question isn't whether to use it but how to use it responsibly and effectively to enhance your career. You'll remain relevant and productive in a rapidly evolving industry by striking this balance.
