How to Use AI Tools for Coding for Beginners
AI coding tools like Claude, ChatGPT, and GitHub Copilot have changed how developers work, but they’re not magic wands that let you code without understanding programming. I’ve used AI extensively while building my projects, including my SaaS application PingBuoy, and I want to share what actually works - and what doesn’t.
Start With the Basics
Before you dive into AI-assisted coding, you need some foundational knowledge. I have computer science coursework in my background, and honestly, that foundation has been essential. Even when AI is writing code for me, I need to understand what it’s doing, spot potential issues, and know when to push back on its suggestions.
If you’re completely new to programming, I strongly recommend taking one of the many free Object-Oriented Programming (OOP) courses available online before relying heavily on AI tools. Even an elementary grasp of programming principles - things like variables, functions, classes, and control flow - will make AI tools dramatically more useful. Without that foundation, you won’t know if the AI is giving you good code or leading you down a problematic path.
What AI Coding Tools Actually Do
AI coding assistants are language models trained on vast amounts of code. They can:
- Generate code snippets based on descriptions
- Explain existing code in plain language
- Debug errors by analyzing error messages and code context
- Suggest improvements or alternative approaches
- Answer programming questions
What they can’t do reliably:
- Understand your entire application architecture without context
- Make complex architectural decisions for you
- Know the latest changes to libraries or frameworks (they have knowledge cutoffs)
- Write production-ready code without human review
- Understand your specific business requirements without clear explanation
Setting Realistic Expectations
When I work with AI tools, I think of them as a knowledgeable pair programmer who sometimes makes mistakes. They’re excellent at:
- Getting past “blank page syndrome” by generating starter code
- Handling boilerplate and repetitive tasks
- Explaining unfamiliar concepts or code
- Suggesting solutions when you’re stuck
But you’re still the senior developer in the relationship. You make the final decisions, you verify everything works, and you’re responsible for understanding the code that goes into your project.
How to Write Effective Prompts
The quality of AI output depends heavily on your prompts. Here’s what works:
Be specific about your context:
- “I’m building a Flask web application…”
- “This needs to work with Python 3.10…”
- “I’m using the requests library version 2.28…”
Describe what you want, not just what to build:
- Instead of: “Make a user authentication system”
- Try: “I need a user authentication function that checks username and password against a SQLite database and returns True if valid, False if not. It should use password hashing for security.”
Share relevant error messages fully: Don’t just say “it’s not working” - paste the actual error message and the code that’s causing it.
Ask for explanations when you don’t understand:
- “Can you explain what this line of code does?”
- “What does this part mean in plain English?”
- “What does the ‘return’ keyword do?”
The Iterative Process
Rarely does AI generate perfect code on the first try. My typical workflow looks like this:
- Initial request - Describe what I need
- Review and test - Run the code, see what works and what doesn’t
- Refinement - Ask for modifications based on testing
- Explanation - Ask about anything I don’t fully understand
- Integration - Adapt the code for my specific project needs
Example iteration - creating a simple contact page (these are example prompts, not real conversations):
- First version: “Create a basic HTML page with a contact form that has name and email fields”
- Second version: “Add a message field where people can type their question”
- Third version: “Make the email field check that it contains an @ symbol before submitting”
- Fourth version: “Add a ‘Thank you’ message that shows up after someone submits the form”
Notice how each step builds on the previous one? That’s iteration - starting simple and gradually adding features.
When to Trust AI vs. When to Verify
Trust AI for:
- Common programming patterns you’re learning
- Simple code structures
- Basic explanations of how code works
- Helping you understand error messages
Always verify:
- Any code that handles passwords or login information
- Code that saves or changes data
- Code that handles user information
- Anything you don’t fully understand
- Code that connects to websites or services
Common Mistakes Beginners Make
Copying code blindly - If you don’t understand what the code does, you can’t maintain it, debug it, or extend it. Always ask for explanations.
Not testing thoroughly - AI-generated code might work for the example case but fail on edge cases (unusual or unexpected inputs). Test with various inputs.
Treating AI as infallible - AI makes mistakes, suggests outdated approaches, and sometimes misunderstands your requirements.
Skipping the learning - The goal isn’t just to get working code, it’s to become a better developer. Use AI to learn, not to avoid learning.
Not providing enough context - AI doesn’t know your full project structure, your dependencies, or your specific requirements unless you tell it.
Learning While Using AI
This is crucial: AI tools should make you a better programmer, not just someone who copies code. Here’s how to learn while using AI:
Ask “why” questions:
- “Why did you write the code this way?”
- “What would happen if I changed this part?”
- “When would I want to do this differently?”
Request alternatives:
- “Can you show me another way to do this?”
- “What’s a simpler approach?”
Study the generated code: Don’t just copy-paste. Read through it line by line. Look up parts you don’t understand. Make sure you could explain what each line does.
Challenge it: If something seems confusing or overcomplicated, ask about it. AI sometimes makes things more complex than they need to be.
Practical Example: Building a Feature
Let me share how I might use AI to build a simple feature. Say I need to create a function that calculates the total price of items in a shopping cart, including sales tax.
Example conversation flow (example prompts):
Me: “I need a Python function that calculates the total cost of items. It should take a list of prices and add 7% sales tax.”
AI: [generates code that adds up the prices and multiplies by 1.07]
Me: “What happens if someone passes in an empty list with no prices?”
AI: [explains it would return 0, which is correct]
Me: “Can you add comments to explain what each part does? I’m still learning.”
AI: [adds explanatory comments to the code]
Me: “How would I test this to make sure it works correctly?”
AI: [suggests test cases with different price lists]
This back-and-forth is normal and productive. You’re not just getting code, you’re learning how it works and how to test it.
Tools Worth Trying
Claude (what you’re using now) - Great for longer conversations and understanding context. Can handle multiple files and complex problems.
ChatGPT - Similar capabilities, good for quick code generation and explanations.
GitHub Copilot - Integrates directly into VS Code and other editors, suggests code as you type. Works differently than chat-based tools.
Each has strengths. I find chat-based tools like Claude better for exploratory work and learning, while Copilot is faster for writing code when you already know what you want.
Final Thoughts
AI coding tools are powerful, but they’re tools - not replacements for understanding. With some foundational programming knowledge and the right approach, they can dramatically accelerate your development and learning.
The key is to:
- Start with basic programming knowledge (take that OOP course!)
- Treat AI as a knowledgeable assistant, not an oracle
- Always understand the code you’re using
- Test everything thoroughly
- Keep learning and asking questions
I built PingBuoy using AI-assisted development, but that was only possible because I had the foundational knowledge to guide the process, evaluate the suggestions, and integrate everything into a working system. AI amplified my abilities - it didn’t replace them.
If you’re just starting out, invest in learning the basics first. Then AI tools will become incredibly valuable collaborators in your development journey.