How to Explain Complex Code to Your Team with AI

Dropping a wall of gnarly code into a PR comment or Slack thread and hoping your teammates follow along is a tax on everyone's time. If you've ever stared at a dense function trying to figure out how to explain it — not just understand it yourself — this walkthrough is for you. I'll show you exactly how to use AI to generate clear, audience-appropriate explanations of complex code, with prompts you can copy and adapt right now.

Why Explaining Code Is Harder Than Writing It

Writing code externalizes your thinking. Explaining it requires you to reconstruct someone else's mental model from scratch. You know what the function does; the challenge is knowing what your reviewer, junior teammate, or on-call engineer doesn't know. That gap is where communication breaks down.

AI closes the gap by letting you generate explanations at multiple levels of abstraction, on demand, in seconds.

Step 1 — Paste the Code and Set the Audience

The single biggest mistake I see is using a generic "explain this code" prompt. The output is almost always pitched at the wrong level. Instead, tell the model exactly who you're writing for.

You are a senior engineer explaining code to a mid-level teammate who knows Python
but has never worked in this service. Explain what the function below does,
why it's structured this way, and what a caller needs to know before using it.
Be concrete. Avoid jargon unless you define it. Keep it under 150 words.

[paste function here]

The constraints (under 150 words, avoid jargon unless you define it) are doing real work. Without them, you get a textbook. With them, you get something paste-able into a PR comment.

Step 2 — Generate a "Mental Model" Summary

Once you have a plain-English explanation, ask for a one-sentence mental model — the kind of thing a teammate will actually retain.

Now give me a single sentence that captures the core mental model for this function.
Format: "Think of this as [analogy or short description]."

Example output for a rate-limiter using a token bucket:

Think of this as a leaky bucket — tokens refill at a fixed rate, and each request spends one; when the bucket is empty, requests are rejected until it refills.

That one sentence does more for team understanding than three paragraphs of inline comments.

Step 3 — Produce a Layered Explanation for Different Readers

On a recent project I was responsible for a service that processed financial events. The same piece of logic needed to be understood by a backend engineer, a QA engineer, and a product manager — all of whom would read the same wiki page. I used this prompt:

Explain the code below at three levels:
1. For a backend engineer: focus on implementation details, edge cases, and performance characteristics.
2. For a QA engineer: focus on inputs, outputs, and failure modes to test.
3. For a non-technical stakeholder: focus on what the code *does for the user* in plain language.

Keep each section clearly labeled and under 100 words.

[paste code here]

The output gives you three sections you can drop directly into docs, tickets, or a wiki — no editing required beyond a quick sanity check.

Step 4 — Ask AI to Surface What's Surprising

This is the step most engineers skip. Complex code has hidden gotchas — assumptions baked in, edge cases the original author knew about but never documented. Use this prompt to surface them:

Read the code below and tell me:
- What would surprise a new engineer reading this for the first time?
- What assumptions does this code make that aren't obvious from the signature?
- What would break if those assumptions changed?

[paste code here]

This pattern is one of the ones I've packaged into The AI Leverage Playbook: 50 Prompts & Workflows for Engineers — but the version above is enough to get real value on its own.

Use the output as the basis for a "Caveats" or "Watch out for" section in your documentation. It takes 30 seconds and saves the next engineer an hour of confusion.

Step 5 — Turn the Explanation into Inline Comments

Finally, close the loop by generating comments you can commit alongside the code:

Using the explanation you just wrote, generate inline comments for the code below.
Rules:
- Comment the "why", not the "what" — assume the reader can read the code.
- Use complete sentences.
- Keep each comment to one line if possible.
- Don't comment every line — only the non-obvious ones.

[paste code here]

This produces comments that are actually useful rather than the noise of // increment i by 1.

Putting It Together

The full workflow is:

1. Audience-targeted explanation → PR comment or Slack message
2. One-sentence mental model → team wiki, README header
3. Layered explanation → wiki page or ticket description
4. Surprises and assumptions → "Caveats" section in docs
5. Inline comments → committed to the repo

None of these steps take more than two minutes each. The total time investment is under ten minutes for documentation that would previously have taken an hour — or never gotten written at all.

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I break down one workflow like this every week in The AI Leverage Weekly — practical, no fluff, free. One concrete AI workflow per engineer, every week. Subscribe: https://theaileverageweekly.beehiiv.com/subscribe?utm_source=devto&utm_medium=article&utm_campaign=long_w18