The ChatGPT Mistake That Stalls Your Engineering Growth

What this guide covers

After this, you’ll have a clear, practical approach to using ChatGPT without letting it replace your own engineering problem-solving and coding skills. You’ll learn how to combine AI-generated ideas with hands-on building, testing, and debugging to grow your technical depth and value.

When to use it

• When tempted to copy-paste ChatGPT code without understanding it
• Debugging errors in code snippets directly taken from ChatGPT
• Learning new cloud services or APIs and validating AI-suggested workflows
• Preparing solutions where critical thinking and troubleshooting are essential

The move, step by step

1. Ask ChatGPT for an outline or draft: Use it to generate ideas, pseudocode, or high-level steps rather than final code.
2. Translate the draft yourself: Write your own code or scripts based on the AI’s suggestions—don’t copy verbatim.
3. Build incrementally: Run small parts of your code as you write it to confirm behavior before moving on.
4. Debug actively: When you encounter errors, spend time troubleshooting using logs and tools instead of asking AI to fix it immediately.
5. Review AI output critically: Question and test every suggestion, especially edge cases or security considerations.
6. Document what you learn: Add comments or notes explaining fixes or insights you uncover during testing.
7. Pair daily practice: Integrate this process into your regular coding or cloud tasks to reinforce your skills.

Example

Input: “Help me write a Python function to parse JSON API responses and handle errors.”

ChatGPT draft:

import json

def parse_response(response):
    try:
        data = json.loads(response)
        return data
    except json.JSONDecodeError:
        return None

Your version:

import json

def parse_response(response):
    try:
        data = json.loads(response)
        if 'error' in data:
            raise ValueError(f"API error: {data['error']}")
        return data
    except json.JSONDecodeError as e:
        print(f"Decode failed: {e}")
        return None

Test run:

resp = '{"name": "test"}'
print(parse_response(resp))  # {'name': 'test'}

resp = '{"error": "invalid request"}'
print(parse_response(resp))  # Raises ValueError with message

You added error handling and tested edge cases instead of using the AI snippet as-is.

Common mistakes

• Mistake: Copy-pasting AI code blindly → Fix: Always rewrite and understand before running.
• Mistake: Asking AI to debug your entire code → Fix: Use your debugger first to isolate issues.
• Mistake: Treating AI output as authoritative → Fix: Validate with official docs or tests.
• Mistake: Skipping incremental builds → Fix: Build and test small chunks step-by-step.
• Mistake: Ignoring edge cases in AI code → Fix: Write your own test cases covering failures.

Next step

Pick one routine work task today—like writing a function, deploying a cloud resource, or configuring a tool. Use ChatGPT only to draft a plan or outline. Then build and test it yourself from the ground up, adding your fixes and learning. Then come back and try the next move from the video.