Explaining Code with ChatGPT

Explaining Code with ChatGPT

Reading unfamiliar code is the daily reality of the developer's job, and ChatGPT is good at producing readable explanations. The trick is to ask for the right level of detail and to verify the explanation against the code.

Ask for Level and Audience

Tell the model your experience level and how deep you want to go. "Explain this to someone new to Python" and "explain the complexity trade-off to a senior" produce very different answers from the same snippet.

Push for a Line-by-Line Walkthrough

A top-level summary can miss subtle behavior. Request a walkthrough that names each variable and each control flow decision in order.

def flatten(items):
    result = []
    for item in items:
        if isinstance(item, list):
            result.extend(flatten(item))
        else:
            result.append(item)
    return result

Prompt: explain this function line by line, state the base case, and estimate its worst-case complexity.

Ask for Analogies and More Examples

If one explanation does not stick, ask for an analogy, then for a different example input, then for the same logic rewritten in another style. Reviewing the same idea from three angles is one of the strongest ways to learn code.

Challenge It With "What If"

Ask what changes when the input is empty, nested four levels deep, or contains non-list values. The answers reveal whether the model really understands the code or is just summarizing it. Follow up each answer by tracing that case by hand.

Use Explain-Then-Verify

Never copy an explanation into a comment or a review without running the code. Run the case, observe the output, and compare with the explanation. When they disagree, the code wins.

Key Points

• Specify your level and the detail you want.
• Request line-by-line walkthroughs for tricky code.
• Use analogies and multiple angles to build understanding.
• Verify explanations by running the code.