Getting ChatGPT to Explain Someone Else's Code Without Surface-Level Summaries

You dropped into a repo you've never touched before — no docs, minimal comments, and a PR review due in two hours. You paste the function into ChatGPT and ask it to explain the code. You get back a tidy paragraph that describes what you can already read. That's not an explanation; it's a transcription.

The problem isn't ChatGPT's capability. It's that the default prompt triggers the default response: a surface-level summary. With the right prompting strategy, you can push ChatGPT into giving you the kind of explanation a senior engineer gives during a pairing session — one that covers intent, tradeoffs, hidden assumptions, and places to be careful.

What You'll Learn

• Why generic explanation prompts produce generic outputs and how to fix that
• Specific prompt patterns that extract intent, assumptions, and failure modes
• How to frame large, multi-file code for ChatGPT without losing context
• How to handle code with no comments, no tests, and no documentation
• How to spot architectural decisions hidden inside plain-looking code

What Makes a Code Explanation Actually Useful

A useful code explanation answers questions you couldn't answer just by reading the code carefully. It tells you why the author made this choice instead of the obvious alternative. It tells you what this function assumes about its inputs that isn't stated in the signature. It tells you what breaks if you touch the wrong thing.

A surface-level summary tells you that filter_active_users filters a list of users and returns only the active ones. You already knew that. What you need to know is: what counts as "active", why the filter happens here instead of at the query layer, and what happens if an entry is missing the status field entirely.

Keep that distinction in mind as you design your prompts. Every word in your prompt should be steering ChatGPT toward the second kind of answer, not the first.

Set the Stage: Give ChatGPT the Right Context First

Before you paste any code, tell ChatGPT what kind of system it's looking at and what your goal is. A blank context produces a generic explanation. A rich context produces a targeted one.

A solid context-setting message looks like this:

I'm onboarding onto a Python 3.11 Django REST Framework codebase. This is a B2B SaaS product. I need to understand a module well enough to safely extend it. I'll paste sections of code and I want you to explain them at the level of intent and architecture, not just line-by-line mechanics.

That one paragraph changes everything ChatGPT will produce afterward. You've told it the language version, the framework, the domain, your goal, and the depth you want. It no longer has to guess, so it stops defaulting to the safest possible response.

If you're working through multiple functions in the same session, you don't need to repeat this. ChatGPT maintains session context. But if you open a fresh conversation, restate it.

Prompt Patterns That Unlock Deeper Explanations

The following patterns consistently produce explanations that go beyond surface-level. Mix and combine them depending on what you need to understand about a specific piece of code.

Ask for the 'Why', Not the 'What'

The single most effective shift you can make. Replace "explain this code" with a question that structurally cannot be answered with a summary.

Don't tell me what this code does line by line. Tell me why the author wrote it this way. What problem is this approach solving, and what alternative approaches would a developer typically consider before landing here?

This forces ChatGPT to reason about design intent. You'll get answers that mention things like "this pattern avoids N+1 queries" or "this is likely doing X because the simpler approach breaks when Y". That's the kind of context that actually helps you work safely in the code.

Ask It to Identify Hidden Assumptions

Every function makes assumptions that aren't in its signature. This prompt surfaces them explicitly.

List every assumption this code makes about its inputs, the state of the system, and the environment it runs in. Flag any assumption that isn't enforced by the code itself.

A good response to this prompt might tell you that the function assumes the database connection is already committed, that it expects timestamps to be UTC-naive even though nothing enforces it, or that it silently returns an empty list on a network failure instead of raising. These are exactly the things that burn you when you extend the code without knowing them.

Ask for Failure Modes and Edge Cases

This prompt is especially useful for code you're about to modify or test.

What are the edge cases and failure modes in this code? For each one, describe what the actual behavior would be — not just that it might fail, but specifically what would happen and whether the caller would even know.

The key phrase is "whether the caller would even know". This pushes ChatGPT past the obvious "it could raise an exception" response and into territory like "this will return an empty dict silently, so the caller will proceed as if the operation succeeded". That's the kind of gotcha you'd only normally catch after a production incident.

This pairs well with the advice in debugging ChatGPT code suggestions that silently break edge cases, which covers the same problem from the opposite angle — catching issues in code ChatGPT generates for you.

Ask It to Describe the Mental Model

When you're looking at a non-trivial algorithm or a stateful class, this prompt helps you build an accurate internal picture fast.

Describe the mental model I should hold when working with this code. What are the core concepts, what invariants does it maintain, and what metaphor or analogy would make its behavior intuitive?

For something like a custom event queue or a state machine, the output from this prompt saves you from building the wrong mental model — which is what causes subtle bugs when you modify the code later.

Breaking Down Large Files Without Losing the Thread

ChatGPT has a context window limit. Pasting a 600-line file and asking for a deep explanation is going to produce a compressed, shallow result because the model has to summarize aggressively just to fit everything in. The fix is to be deliberate about what you send and in what order.

Start with structure before detail. Send class or module signatures without the method bodies first:

Here's the structure of the module — class definitions and method signatures only, no bodies. Tell me what this module is responsible for architecturally and what each class seems to own.

Then drill into individual methods one at a time, carrying forward what ChatGPT already established about the overall design. This way the model has working memory of the architecture when it explains each piece, and the explanations stay coherent instead of treating each function as if it exists in isolation.

For long sessions, explicitly summarize before moving on: "Based on what we've discussed, here's my current understanding — correct anything wrong before I paste the next section." This keeps accumulated errors from compounding.

When the Code Has No Comments or Tests

No comments and no tests means no intent signal beyond the code itself. ChatGPT can still help, but you need to point it at the right signals.

Ask it to infer purpose from naming, structure, and dependencies:

There are no comments or tests in this code. Based purely on variable names, function names, the libraries imported, and the structure of the logic, what do you think the original author intended this to do? Where is your confidence lower?

The "where is your confidence lower" phrase is important. It gets ChatGPT to flag the parts where it's reasoning from thin evidence, so you know which sections of the explanation to verify more carefully yourself rather than taking at face value.

If you have related code nearby — a calling function, a test for something adjacent, a serializer that uses this model — paste that too. The surrounding context often contains intent signals the target function doesn't.

If you need to also generate tests for this kind of uncommented code, the guide on using Copilot in VS Code to write tests for untested legacy functions walks through a complementary approach.

Spotting Architectural Decisions in Plain Code

Some of the most important things to understand about unfamiliar code are decisions the author made that look like implementation details but are actually architectural constraints. A custom retry loop instead of a library. A manual join instead of an ORM relationship. An in-memory cache using a module-level dict instead of Redis.

These choices are almost never documented, but they have consequences for anything you build on top of them. Use this prompt to surface them:

What architectural or design decisions are embedded in this code that someone extending it would need to understand? Focus especially on places where the author chose a non-obvious approach — anything that deviates from what a developer might write by default.

You can push further by asking ChatGPT to categorize what it finds: "For each decision, tell me whether it looks intentional and deliberate, or whether it looks like it might be accidental or legacy debt." That distinction shapes how cautiously you touch each part.

This kind of structured AI-assisted review overlaps with the techniques in getting useful code reviews from ChatGPT without generic feedback — worth reading if you want to extend this into a full review workflow.

Common Pitfalls When Using ChatGPT for Code Explanation

Trusting confident-sounding wrong answers. ChatGPT can produce a fluent, detailed explanation that is simply incorrect about what a piece of code does — especially with unusual patterns, older library APIs, or domain-specific logic. Always verify non-obvious claims by running the code, reading the actual library docs, or checking git history.

Pasting too much at once. More code does not produce a better explanation. It produces a more compressed one. Smaller, focused pastes with explicit questions produce better results every time.

Asking one question and stopping. The first explanation ChatGPT gives is a starting point. Follow up. "You said this function assumes X — what happens in the code if that assumption is violated?" Follow-ups consistently produce the most useful information in any code explanation session.

Not specifying the language version or framework. ChatGPT knows Python 2 and Python 3, Django 2 and Django 4, and dozens of historical API shapes. Without version context, it may explain code against the wrong version of a library and get behavior subtly wrong.

Ignoring the limits of what ChatGPT can know. ChatGPT doesn't have access to your runtime environment, your database schema, your environment variables, or your production data. For explanations that depend on runtime state — like why a query returns zero results — you need to supply that context explicitly, or the explanation will be guesswork.

The same caution applies when you're using ChatGPT to speed up related tasks: the guide on speeding up SQL query writing with ChatGPT without trusting it blindly covers how to maintain that critical verification layer in another common use case.

Wrapping Up: Next Steps

Surface-level summaries are ChatGPT's default because surface-level questions are what most people ask. You now have a set of prompts that change what you ask — and as a result, what you get back.

Here's what to do next:

• Start your next unfamiliar codebase session with a context frame. One paragraph about the stack, domain, and your goal before you paste a single line of code. This is the highest-leverage change you can make.
• Build a personal prompt library. Save the prompts that produced the best explanations for you. Paste them as starting points in future sessions rather than writing from scratch each time.
• Layer your questions. Start with structure, then zoom into individual functions, then drill into assumptions and failure modes. Treat each session as a structured investigation, not a single query.
• Verify architectural claims independently. When ChatGPT tells you a design decision was made for a specific reason, check git blame, check PRs, or ask a teammate. Use ChatGPT's explanation as a hypothesis to validate, not a conclusion to accept.
• Combine with other tools. ChatGPT explanations pair well with Copilot-generated tests and structured docstrings. If you want to go deeper on docstring generation, see generating accurate docstrings with Copilot without editing every output.