AI Prompts That Help Explain Unfamiliar Code

Opening an unfamiliar file and staring at code you did not write is a normal part of programming. It might be a function a colleague left behind, a snippet copied from a forum answer, or a library example that almost fits your problem. Asking an AI chatbot to explain it can save a lot of time, but only if you ask well. A vague prompt like "explain this code" usually returns a vague answer: a surface-level summary that sounds confident and teaches you very little.

This guide shows you how to write AI prompts that actually explain unfamiliar code. You will learn what context to include, how to ask for a step-by-step explanation, and how to check the answer before you trust it. The worked example uses a short JavaScript function that you can follow even if JavaScript is not your main language.

Provide safe code context first

An AI model can only explain what it can see. If you paste a single function with no background, it has to guess what the inputs mean, what the function is for, and which parts matter to you. A few lines of context remove most of that guessing.

Before you paste any code, add these four things to your prompt:

• The language and version, if you know it. "This is JavaScript" is enough. If the code uses a framework, name it, for example "React" or "Node.js with Express".
• What the code is supposed to do. One sentence is plenty: "This function formats a price for display on a checkout page." That sentence tells the model what to check its explanation against.
• Where the code came from. "It is from a legacy checkout module" or "I copied it from a tutorial" changes how the explanation should be pitched.
• Your level and your goal. "Explain it as if I know basic JavaScript but have never used the Intl API" produces a very different answer from "I am an experienced developer; focus on the edge cases."

Keep the pasted code short. If a file is hundreds of lines long, do not paste all of it and ask for "an explanation". Trim it to the function or block you actually need to understand, and mention what the rest of the file does in one line.

One safety rule matters more than any prompt trick: never paste secrets into an AI chat. Before you copy code, remove API keys, passwords, tokens, private URLs, customer names, and internal server addresses. Replace real values with clearly fake ones, such as API_KEY_HERE. The examples in this article use only invented data, and you should do the same.

Ask for a stepwise explanation, not a summary

The single most useful change you can make to an "explain this code" prompt is to tell the model exactly how to structure its answer. Ask for an overview first, then a walkthrough, then a trace with real values.

Here is a prompt template you can copy and adapt:

Explain the following code to me. I know basic JavaScript but I am
new to this codebase.

Code:
[PASTE YOUR CODE HERE]

Please:
1. Give a one-paragraph summary of what the code does and why it exists.
2. Walk through it step by step, explaining each significant block
   and any line that is not obvious.
3. Explain any language feature or built-in API I might not know.
4. Trace what happens with this example input: [EXAMPLE INPUT].
5. Point out any bugs, edge cases, or things that look risky.
Use plain language and define any jargon you use.

Each part of that template does a specific job. The summary gives you the big picture before the details. The walkthrough slows the model down so it explains the tricky lines instead of skipping them. The trace is the most valuable part: when the model works through a concrete input, mistakes in its reasoning become visible, because you can check the trace against the code and against what the program actually does when you run it.

If the first answer is still too shallow, follow up with a narrower question instead of repeating the whole prompt. Good follow-ups include: "What does this line do, and what would break if I removed it?", "What happens when the input is empty?", and "Why is this check here — what problem does it prevent?"

Worked example: explaining a short JavaScript function

Here is a short, real JavaScript function that formats a price stored in cents into a display string. Read the prompt, then the explanation, and notice how each part of the prompt shapes the answer.

function formatPrice(cents, currency) {
  if (!Number.isInteger(cents) || cents < 0) {
    throw new Error("Price must be a non-negative whole number of cents.");
  }
  const amount = cents / 100;
  return new Intl.NumberFormat("en-US", {
    style: "currency",
    currency: currency
  }).format(amount);
}

The prompt for this function would be:

Explain this JavaScript function. It formats a price for a checkout
page, and the price is stored in cents to avoid decimal rounding
problems. I know basic JavaScript but I have never used Intl.

[PASTE THE FUNCTION]

Please give a one-paragraph summary, a step-by-step walkthrough,
a trace for formatPrice(1999, "USD"), and any edge cases I should
know about.

A good explanation should cover these points, and you can use them as a checklist for any answer you receive:

• Purpose: the function turns an integer number of cents and a currency code into a formatted money string, for example 1999 cents becomes "$19.99".
• The guard clause: the if at the top rejects anything that is not a whole, non-negative number. Number.isInteger returns false for decimals, strings, and other non-integers, and the || means either bad condition triggers the error. This protects the function from silently formatting nonsense.
• The conversion: dividing by 100 turns cents into major currency units: 1999 / 100 = 19.99.
• The formatting: Intl.NumberFormat is a built-in JavaScript API that formats numbers according to locale rules. With "en-US" and style: "currency", it adds the dollar sign, the decimal point, and exactly two decimal places.
• The trace: calling formatPrice(1999, "USD") passes the guard (1999 is an integer and not negative), computes 19.99, and returns the string "$19.99". Calling formatPrice(-5, "USD") throws the error instead of returning a negative price.
• Edge cases: the function accepts any non-negative integer, so formatPrice(0, "USD") returns "$0.00". It does not validate the currency code itself, so an invalid code like "XXX" would cause Intl.NumberFormat to throw its own error. It also always uses the "en-US" locale, so it will not automatically switch formats for other countries.

Notice what a careful explanation does: it does not just restate each line in different words. It says why the line exists, what would go wrong without it, and what happens at the boundaries. When you read an AI explanation, hold it to that standard. If it only paraphrases the code, ask a follow-up: "Why is this check necessary? Show me an input that would fail without it."

Verify the interpretation before you trust it

AI explanations can be wrong in ways that sound convincing. The model may misread an operator, invent a behavior for a library function, or describe what the code should do rather than what it actually does. Treat every explanation as a claim to check, not a fact to accept.

Three quick checks catch most mistakes:

1. Run the trace yourself. Take the example input from the explanation and run the code, or work through it by hand. In the example above, you can confirm in seconds that formatPrice(1999, "USD") really returns "$19.99" and that a negative input really throws. If the trace does not match reality, the explanation is wrong somewhere, even if the rest of it reads well.
2. Test the edge cases it mentions. If the model says "this handles empty input", try empty input. If it says "this never throws", look for the input that might prove otherwise. Edge-case claims are where confident-sounding errors hide.
3. Cross-check library claims. When an explanation describes what a built-in function or library call does, verify it against the official documentation.

If a check fails, paste the failing result back to the model and ask it to explain the discrepancy. That loop — explain, test, correct — is where real understanding comes from.

There is one more verification habit worth building: after you understand a function, try explaining it back in your own words, in one or two sentences, without looking at the AI answer.

Prompts for common situations

Different situations call for different prompts. Keep these variations handy:

• You inherited a whole file: "List the main parts of this file, what each one is responsible for, and how data flows between them. Start with the entry point and follow one request through the code."
• You found a confusing line: "Explain only this line. What does each part do, and what would break if I changed or removed it?"
• You suspect a bug: "This code is supposed to do [EXPECTED BEHAVIOR], but it does [ACTUAL BEHAVIOR]. Walk through it and point out where the two diverge."
• You are learning a new language feature: "Explain this code as if I am new to [FEATURE]. Give a small example that shows the feature on its own, separate from the rest of the code."
• You need to explain it to someone else: "Summarize this code in three short paragraphs I can paste into a code review: what it does, why it is written this way, and what to watch out for."

Troubleshooting: when the explanation still does not click

Sometimes even a good prompt returns an answer that does not help. Here is how to fix the common failures:

• The answer is too generic. You probably pasted too much code or gave too little context. Trim to one function and add the one-sentence purpose.
• The answer skips the part you care about. Name the exact lines: "Explain lines 3 to 6 in detail" works better than "explain it more".
• The answer contradicts itself. Point to the contradiction and ask which part is correct. Contradictions usually mean the model guessed at something; forcing it to choose often surfaces the guess.
• The answer uses jargon you do not know. Add "define every technical term the first time you use it" to your prompt, or follow up with "explain that term as if I am a beginner".
• The answer is too long to read. Ask for a shorter format: "Give me the summary in five bullet points, then the walkthrough only for the tricky lines."

Frequently asked questions

Should I paste my whole project into the chat?

No. Paste only the code you need explained, plus a one-line note about what the rest does.

Can I trust an AI explanation of code I cannot run?

Trust it less, and verify it more. Read the explanation against the code line by line, check library behavior in the official docs, and ask the model to trace a concrete input. If you cannot run the code at all, a careful trace is your best substitute for a test.

What if the AI explanation and my reading of the code disagree?

Assume neither is automatically right. Work through a concrete input by hand, or run the code if you can. The disagreement usually points at exactly the line you most need to understand.

Is it safe to paste company code into an AI chatbot?

Only if your workplace allows it, and only after removing secrets and sensitive data. When in doubt, replace real names, identifiers, and values with fake ones, or ask about a small, rewritten example that shows the same pattern instead of the real code.

Conclusion

Good AI code explanations come from good prompts: context, structure, and a concrete example to trace. Ask for a summary, a step-by-step walkthrough, and a trace with values. Then verify the answer by testing the trace and edge cases. For the general basics, see our guide to writing clear AI prompts for everyday work.