The ChatGPT mistake that slows your engineering growth

What this guide covers

After reading this, you’ll know how to use ChatGPT effectively as a tool to speed up coding and documentation while actively sharpening your engineering skills through debugging and testing.

When to use it

• Speed up writing routine code snippets or boilerplate without blindly trusting output
• Quickly triage bugs by generating initial hypotheses or simple scripts, then verify with tests
• Draft documentation or design notes, then critically review and improve them yourself
• Enhance your workflows by combining ChatGPT suggestions with strong local debugging

The move, step by step

1. Frame the problem clearly before asking ChatGPT.
   Define what you want it to do in one sentence to avoid vague or overly broad responses.

2. Use ChatGPT to generate a draft solution or code snippet.
   For example, ask it for a function skeleton, a debugging checklist, or a triage helper.

3. Run the output locally and add manual tests covering edge cases.
   Don’t accept its code as correct without validating functionality and handling errors.

4. Debug any issues by stepping through or logging to understand behavior.
   Use print statements, debugger, or tracing to confirm assumptions and find faults.

5. Compare ChatGPT’s output to official documentation or best practices.
   For example, cross-check API usage against AWS docs or OWASP guidelines.

6. Iterate on the code or explanation yourself instead of just regenerating.
   Challenge the AI’s output and refine it; this is where actual skill growth happens.

7. Document your reasoning and learning points as comments or notebooks.
   Write down why you changed something or how a bug was fixed to build your knowledge base.

Example

Input: “Write a Python function to triage bug severity by alerting the team if severity >= 3.”

ChatGPT output:

def triage_bug(report):
    if report.severity >= 3:
        alert_team()
    else:
        add_to_backlog()

Step: You add a test to verify behavior:

class BugReport:
    def __init__(self, severity):
        self.severity = severity

alerts = []
def alert_team():
    alerts.append("alert sent")

def add_to_backlog():
    alerts.append("added to backlog")

report = BugReport(4)
triage_bug(report)
assert alerts[-1] == "alert sent", "High severity should alert team"

report = BugReport(2)
triage_bug(report)
assert alerts[-1] == "added to backlog", "Low severity should add to backlog"

Expected output: No assertion errors; behavior matches intended triage logic.

Common mistakes

• Mistake: Accepting ChatGPT’s code as-is → Fix: Always write and run tests to verify correctness.
• Mistake: Using AI to replace problem-solving → Fix: Use AI suggestions as starting points, not answers.
• Mistake: Ignoring debugging after generation → Fix: Step through code to understand runtime behavior.
• Mistake: Blindly following AI code style or patterns → Fix: Adapt output to your team’s standards and best practices.
• Mistake: Regenerating output repeatedly without learning → Fix: Invest time improving and refactoring manually.

Next step

Pick a small task you’re working on today and generate a draft solution from ChatGPT. Then write at least two tests for that output and debug any failing cases. Document what you learned in comments or a notebook. Then come back and try the next move from the video.