How to Use AI to Explain Code You Don’t Understand
Reading code is a bigger part of programming than writing it. You inherit a legacy module at work, clone an open-source project to fix one bug, or open a tutorial's final source file and feel completely lost. AI has become one of the fastest ways through that fog, provided you know how to ask and how to check the answers. Using AI to explain code means giving a model a piece of unfamiliar code plus its surrounding context, then asking it to describe the purpose, the flow of data, and the reasoning behind each part, so you build your own accurate mental model rather than borrowing the AI's.
Quick Answer: Paste the code with context: the language, framework, what the project does, and what you already understand. Ask for a layered explanation, starting with the overall purpose, then the data flow, then line-by-line detail on confusing parts. Verify claims by running the code, adding print statements, or checking documentation.
Why Is Unfamiliar Code So Hard to Read?
Code is hard to read because it compresses decisions. A single line might rely on a language feature you haven't learned, a framework convention that's never stated, a helper function defined three files away, and a business rule that exists only in someone's head. Reading it means reconstructing all of that context, which is exactly what AI is good at helping with: it has seen enormous amounts of code and can recognize idioms, patterns, and library conventions instantly.
A few terms frame the techniques in this guide. Control flow is the order in which statements execute, including branches and loops. Data flow is how values move and change through the program: where a variable comes from, what transforms it, and where it ends up. An idiom is a common pattern specific to a language, like Python's list comprehensions or JavaScript's optional chaining. And a side effect is anything a function does beyond returning a value, such as writing to a file, changing global state, or calling an API.
Explanation and debugging are closely linked skills. You can't fix what you don't understand, and many bugs become obvious once the code's actual behavior is clear. If your goal is to fix something, start by understanding it with the techniques below, then move on to using AI to debug without blind pasting, where you test hypotheses rather than applying guesses.
A Layered Prompting Method That Works
The biggest mistake is asking "explain this code" and receiving a wall of line-by-line commentary that's as confusing as the code itself. Instead, move from the big picture to the details in layers.
- Give context first. "This is a Django view from an e-commerce app. I know basic Python but not Django. I need to change how discounts are applied." Context lets the AI calibrate depth and focus.
- Ask for the purpose in two sentences. "In two sentences, what is this code's job and when does it run?" If the summary doesn't make sense, nothing after it will either.
- Trace the data flow. "What are the inputs and outputs? Trace how the variable
totalchanges from start to finish." This is often the single most clarifying question. - Identify unfamiliar features. "List any language features, idioms, or library functions a beginner might not know, with a one-line explanation each."
- Zoom into confusing lines. "Explain lines 14 to 22 in detail. Why might the author have written it this way instead of a simpler loop?"
- Check side effects and risks. "Does this code modify anything outside the function? What could break if I change the discount calculation?"
- Test your understanding. "I think this function does X because of Y. Am I right? Correct me precisely if not." Explaining it back is the fastest way to find gaps.
Step seven matters most. When you restate the code in your own words and the AI corrects a specific misunderstanding, the lesson sticks far better than any explanation you passively read.
Prompt Templates for Different Situations
Different situations call for different questions. The templates below cover the most common reasons people need code explained.
| Situation | Prompt Template | What You Get |
|---|---|---|
| New codebase at work | "Here's the folder structure and main entry file. Explain how a request flows through this app, step by step." | An architectural map before diving into details |
| Dense one-liner | "Rewrite this line as verbose, step-by-step code with comments, then explain why the compact version is equivalent." | A readable expansion you can compare with the original |
| Unfamiliar language | "I know JavaScript well. Explain this Go function by comparing each construct to its JavaScript equivalent." | Explanations anchored in what you already know |
| Regex or SQL | "Break this regular expression into parts and show three example strings that match and three that don't." | Concrete examples that make abstract syntax tangible |
| Tutorial code | "Explain why this tutorial uses useEffect here and what would happen if I removed it." | The reasoning behind design choices, not just mechanics |
Analogies help too. Asking "explain this using a real-world analogy, then point out where the analogy breaks down" gives you an intuitive handle plus a warning about its limits.
How Trustworthy Are AI Code Explanations? Practitioner Analysis
In my experience reviewing how developers learn with AI, explanations of common, self-contained code are usually accurate, while explanations that depend on missing context are where errors creep in. This is practitioner analysis rather than a formal measurement, but the pattern is consistent.
AI explains standard library usage, common framework patterns, algorithms, and language idioms very well. It becomes unreliable when the code calls functions it can't see, depends on configuration or environment, uses an obscure or very new library version, or encodes business rules. In those cases, models tend to fill gaps with plausible assumptions and present them as facts. A function named calculate_tax will be explained as calculating tax, even if it actually also logs analytics and updates a cache.
The practical defense is verification. Run the code with a small input and print intermediate values. Step through it with a debugger. Read the official documentation for any library function the AI explained. Ask the AI directly: "Which parts of your explanation are assumptions because you can't see the rest of the code?" Good models will flag them when asked.
There's a learning dimension too. Beginners who are learning Python with an AI tutor benefit most when they predict what code will do before asking for an explanation. The prediction forces active thinking, and the gap between your prediction and the actual behavior is precisely where learning happens.
Finally, be mindful of confidentiality. Explaining open-source or tutorial code carries little risk, but proprietary code may be covered by company policy. Use an approved tool, or strip sensitive details before pasting.
Common Mistakes When Asking AI to Explain Code
Pasting without context. The same code means different things in different frameworks. Always state the language, framework, and what the project does.
Pasting too much at once. A thousand-line file produces a shallow summary. Start with the overall structure, then explain one function at a time.
Reading passively. Nodding along to an explanation creates false confidence. Close it and explain the code back in your own words.
Not running the code. An explanation is a claim. Execution is evidence. Add print statements or breakpoints to confirm behavior.
Ignoring the "why." Knowing what code does isn't enough to change it safely. Ask why it was written that way; there may be a constraint you'd otherwise break.
Letting AI rewrite instead of explain. If you ask for an explanation and get a refactored version, redirect: "Don't change the code. Help me understand the existing version."
Walkthrough: Understanding an Unfamiliar Function
Here's a realistic scenario. You've joined a team and need to modify a JavaScript function called groupOrders that uses reduce, optional chaining, and nullish coalescing, none of which you know well.
Context prompt: "This function is from a Node.js backend for an online store. I'm comfortable with JavaScript loops but not reduce or modern syntax. I need to add a filter so cancelled orders are excluded. Explain the purpose in two sentences first."
Purpose: the AI explains that the function takes a list of orders and returns an object grouping order totals by customer ID. Clear enough to continue.
Data flow: you ask it to trace two sample orders through the function. It shows the accumulator object starting empty, gaining a key for the first customer, and adding to that key's total for the second order from the same customer.
Unfamiliar syntax: it explains that order.customer?.id safely returns undefined if customer is missing, and that ?? 0 substitutes zero only when the left side is null or undefined.
Your restatement: "So reduce builds the grouped object one order at a time, and if an order has no customer, it gets grouped under undefined?" The AI confirms and notes this edge case might be a latent bug worth raising with the team.
Verification: you run the function with three test orders, including one without a customer, and log the output. It matches the explanation. Now you add the cancelled-order filter confidently, and you've also found a real issue to discuss.
Checklist for any explanation session: context given, purpose understood, data traced, unfamiliar features listed, understanding restated, behavior verified by running code.
Key Takeaways
- Give AI context about the language, framework, project, and your skill level before asking it to explain code.
- Layered explanations that move from purpose to data flow to line-level detail are far clearer than a single line-by-line dump.
- Restating the code in your own words and asking AI to correct you is the fastest way to find gaps in understanding.
- AI explanations become unreliable when code depends on files, configuration, or business rules the model cannot see.
- Treat every explanation as a claim and verify it by running the code, logging values, or checking official documentation.
Frequently Asked Questions
Can AI accurately explain any code?
AI explains common, self-contained code accurately most of the time, including standard libraries, popular frameworks, and well-known algorithms. Accuracy drops when the code depends on files, configuration, or business logic the model can't see, or uses obscure libraries. Always verify important claims by running the code.
What is the best prompt to explain code with AI?
Start with context: language, framework, project purpose, and your experience level. Then ask for the code's purpose in two sentences, followed by a trace of how key data changes. Finish by restating your understanding and asking the AI to correct any mistakes precisely.
Is it cheating to use AI to understand code?
No. Using AI to understand code is similar to asking a senior colleague or reading documentation. It becomes a problem only in settings with explicit rules, such as some exams or courses. In professional work, quickly understanding unfamiliar code is a valued skill, however you achieve it.
How do I explain a large codebase with AI?
Don't paste everything at once. Share the folder structure and entry points first and ask for an architectural overview. Then explore one flow, such as a single user request, file by file. Editor-integrated assistants that index your repository can help, but focused questions still work best.
Can AI explain code in programming languages I don't know?
Yes, and it's especially effective when you ask it to compare constructs to a language you already know. For example, ask it to explain Rust ownership using analogies from Python or JavaScript, then point out where the analogy breaks down. Verify by running small examples yourself.
Should I paste company code into an AI tool?
Check your company's policy first. Many organizations restrict which AI tools may process proprietary code, and some provide approved internal assistants. When unsure, remove secrets, credentials, and identifying details, or recreate the pattern in a minimal example that doesn't expose confidential logic.
Conclusion
The key insight is that AI explanations are most valuable when they help you build your own mental model, not when they replace one. Give context, move from the big picture to the details, restate what you learned, and verify it by running the code. Each session then leaves you a better reader of code, which compounds over your entire career. Apply the same layered approach to database work by pairing it with beginner SQL prompt patterns, since queries are one of the most common kinds of code developers inherit without explanation.
