AI as a Coding Assistant – Understanding Errors, Finding Solutions, Saving Boilerplate

Section 1 – Getting Started: What AI Can and Can’t Do

AI can:

• Explain error messages in plain language
• Rewrite or comment your code
• Suggest examples and test cases
• Help you plan and document projects

AI can’t (and shouldn’t):

• Replace your own judgment
• Write secure production code without review
• Handle private credentials or secrets

❗Remember: AI is your helper, not your replacement.

Section 2 – Understanding Errors (Debugging with AI)

1. Run your code until an error appears.
2. Copy only the error message and the relevant code part.
3. Ask AI:
   “Please explain this error in simple terms. Why does it happen, and how can I fix it?”
4. Follow up with questions like:
   “What does ‘NoneType object is not subscriptable’ mean? Show me a small example.”
5. Try one fix at a time and test after each step.

💡Tip: Ask for alternative solutions (“Is there a cleaner or safer way?”).
That way, you learn instead of just patching.

Section 3 – Finding and Understanding Solutions

Don’t just ask AI to “fix my code.”
Instead, guide the conversation like a mentor session:

• “I’m getting this error on line 23. What’s happening behind the scenes?”
• “Can you show me a commented version of my code?”
• “What’s the difference between this and your suggested version?”

Step-by-step workflow:

1. Describe the problem
2. Ask AI to explain the concept
3. Generate an example solution
4. Clarify confusing parts
5. Implement and test

AI becomes not just a code machine, but a teacher who explains concepts.

Section 4 – Saving Boilerplate Code

Boilerplate code means repetitive patterns you use again and again — models, forms, serializers, setup scripts.

Examples:

• “Create a Python class for a blog post model with title, content, and published date.”
• “Generate a Django model, serializer, and viewset for an article.”
• “Write a React component with prop validation.”

After generation:

• Review every line of code.
• Adjust names, paths, and imports.
• Add comments and docstrings.

Typical time saved: 40–60% on repetitive coding tasks.

Section 5 – Quality and Safety

Checklist for clean AI-assisted code:

• Do I understand what the code does?
• Did I test it locally?
• Are libraries up to date and secure?
• Did I remove all personal or sensitive data?
• Did I document that AI helped me create this code?

Section 6 – Common Pitfalls

Section 7 – Useful Prompts (Templates)

1. Explain an error:
“I’m getting this Python error: [error message]. Explain in simple terms what it means and show how to fix it.”

2. Comment code:
“Add clear line-by-line comments to this code so I can understand what happens.”

3. Write tests:
“Generate basic unit tests for this function.”

4. Refactor code:
“Simplify this code without changing its logic. Make it cleaner and easier to read.”

5. Compare approaches:
“Show me two ways to solve this problem — one simple, one more efficient — and explain the difference.”

Section 8 – Learning Strategy with AI

1. Start small: ask about one error or function at a time.
2. Understand before copying: ask AI to explain, not just code.
3. Experiment: compare different solutions.
4. Reflect: summarize what you learned after each session.

Over time, AI becomes your study partner – not a shortcut.

Section 9 – Data Privacy and Intellectual Property

• Don’t paste private or client code into public AI tools.
• Use company-approved or local AI setups for sensitive work.
• Check licenses for any AI-generated snippets.
• Add a short note: “Created with AI assistance (tool name, date).”

Section 10 – Mini Self-Test

1. Do I understand every part of the AI’s code?
2. Did I test and comment it properly?
3. Did I remove any sensitive or private data?
4. Did I check libraries and licenses?
5. Did I learn something new from this task?

If you answered “yes” to four or more — you’re doing great!

Checklists & Examples

Checklist before committing code:

• All tests pass
• No private data in the code
• Comments and docstrings added
• Style consistent (PEP8, ESLint, etc.)
• Code reproducible and reviewed

Example workflows:

• Debugging with ChatGPT
• Autocomplete with GitHub Copilot
• AI-assisted code review
• Automatic setup for REST APIs or UI components