How to Understand an Unfamiliar Codebase With AI

The fastest way to understand a codebase with AI is not to paste the whole repository and ask “what does this do?”. It is to use the assistant like a patient senior colleague: first for a map of the project, then for guided reading of one path through the code, and finally for checking your own understanding. Done in that order, a week of confused reading on a new job or an inherited project can shrink to a couple of focused days.

This guide walks through that process step by step, with prompts you can reuse, a comparison of approaches, and the places where AI explanations of code go wrong.

Why an unfamiliar codebase is hard to read

Reading someone else’s code is hard for reasons that have little to do with syntax. The difficulty is missing context: why the project is structured the way it is, which folders matter, what the naming conventions mean, which parts are dead code and which are critical. The original authors carried that context in their heads. You have to rebuild it from the files.

Most people start at the top of the folder tree and read file by file. That is the slowest possible route, because you spend hours on configuration, helpers and tests before you ever see how a request travels through the system. AI helps most when it changes the order in which you read, not when it replaces reading altogether.

Step 1: Get a map before you read any code

Start with the shape of the project, not its contents. Run a directory listing, limited to two or three levels, and give it to the assistant together with the README, the dependency file (such as package.json, requirements.txt, pom.xml or go.mod) and any Docker or deployment configuration.

A prompt for the first map

“Here is the folder structure, README and dependency list of a project I have just inherited. Tell me: what kind of application this is, what the main entry points probably are, which folders hold business logic versus infrastructure, and which five files I should read first. Mark anything you are guessing.”

The last sentence matters. At this stage the assistant is inferring from names and dependencies, and you want to know which parts are educated guesses. A folder called services could hold anything.

Check the map against reality

Before you rely on the map, confirm two or three claims yourself. Open the file the assistant called the entry point and check that it really starts the application. If it was wrong about something basic, give it the correction and ask for a revised map. This takes ten minutes and saves you from building a mental model on a wrong foundation.

Step 2: Follow one real path through the code

Once you have a map, pick one concrete thing the software does and follow it from start to finish. Good choices are a login, a checkout, an API endpoint that you know is used, or a scheduled job. One complete path teaches you more about how the codebase is organised than reading twenty unrelated files.

Trace a request

Give the assistant the file where the path starts, such as a route definition or controller, and ask: “Walk me through what happens when a user submits the login form, starting from this file. For each step, tell me which function is called next and in which file I will probably find it.” Then open that next file, paste it in, and continue. You stay in control of what gets read, and the assistant keeps the thread.

Ask for a diagram

After two or three files, ask for a sequence diagram or flowchart of the path so far. Seeing the flow drawn out often reveals that two layers do nearly the same thing, or that a call goes through a queue you had not noticed. In Ask Mio, the diagram and flowchart generator can turn that description into an editable HTML or SVG diagram you can keep in your notes.

Step 3: Decode the project’s own vocabulary

Every codebase has its own dialect. Domain terms like “tenant”, “ledger entry” or “fulfilment slot”, abbreviations in variable names, and patterns the team invented all slow you down until you know them.

Collect the terms you do not understand as you read, then ask the assistant to build a glossary: “Based on these files, explain what each of these terms means in this project, and point to where each one is defined.” Keep the glossary in a project note. It becomes the document you wish had existed on day one, and the next new colleague will thank you for it.

Recognise patterns, not just names

Ask the assistant to identify the architectural patterns in use: “Is this a repository pattern? Where is dependency injection configured? Why do these classes all end in Handler?” Naming a pattern connects the unfamiliar code to something you may already know from other projects.

Step 4: Use version history as context

Code tells you what the system does. History tells you why. Two commands give an assistant a lot of useful context:

• git log for one file or folder shows how it evolved and which changes were large.
• git blame on a confusing function shows which commit introduced each line, and the commit message often explains the odd bit.

Paste the relevant log output together with the code and ask: “Given this history, why do you think this function has three different early returns?” Often the answer is a bug fix or a customer-specific exception, which is exactly the kind of context that is invisible in the code itself.

Approaches compared: which way of reading works best

There is more than one way to understand a codebase with AI, and each has trade-offs. The table compares the common approaches.

Approach	Best for	Main weakness	Speed	Accuracy risk
Reading files top to bottom, no AI	Very small projects	Slow, no sense of priority	Slow	Low
Pasting the whole repository into one chat	Tiny codebases only	Context limits, shallow summaries	Fast	High
Map first, then one path at a time with AI	Most real projects	Needs discipline to verify	Fast	Low to medium
Asking the original authors	Design decisions, history	People are busy or gone	Varies	Low
Writing tests to probe behaviour	Critical logic you must change	Takes time to set up	Medium	Very low

The winning combination is usually the third row plus the last one: use AI to get oriented quickly, then write small tests around anything you are about to change.

Why “paste the whole repo” usually disappoints

It is tempting to zip the entire project and ask for an explanation. For a small tool this can work. For anything larger, three problems appear.

First, there is a limit to how much text an assistant can consider at once. Our explainer on AI context windows covers this in detail, but in short: even when a large repository fits, attention is spread thinly and the summary tends to describe the folder names rather than the logic. Second, a big summary is hard to verify, so errors slip through. Third, you learn less. Understanding comes from following specific paths and asking specific questions, not from reading a summary of everything.

A better use of large uploads is targeted: upload the repository, then ask narrow questions such as “where is the discount calculated?” or “which files import the payment client?”. That turns the assistant into a search tool that explains what it finds.

Step 5: Test your understanding before you change anything

The real test of understanding is whether you can predict what the code will do. Before making your first change, explain the path you traced back to the assistant in your own words and ask it to find mistakes: “Here is my understanding of how order totals are calculated. What have I got wrong or left out, based on the code I showed you?”

Then ask for edge cases: “What happens if the cart is empty? If a discount code has expired? If the currency is different?” Where the answer is not obvious, write a small test and run it. The test settles the question, and it protects you when you start changing things. If you are about to restructure old code, our guide on refactoring legacy code with AI picks up from here.

Where AI explanations of code go wrong

AI assistants are good at explaining code, but they fail in predictable ways. Knowing them keeps you from trusting the wrong answer.

• Confident guesses about unseen files. If the assistant has not seen a function, it may describe what that function “probably” does based on its name. Ask it to say explicitly when it has not seen the code.
• Framework magic. Behaviour configured by convention, annotations or middleware is easy to miss. If a request seems to skip a step, ask where the framework might be injecting behaviour.
• Dead code treated as live. An assistant cannot tell from one file whether a function is still called. Search for usages yourself.
• Outdated comments. Comments may describe what the code did years ago. When a comment and the code disagree, believe the code and ask the assistant to explain the code only.
• Version assumptions. Library behaviour changes between versions. Give the assistant the exact versions from your dependency file.

How Ask Mio helps you understand a codebase with AI

Ask Mio’s Code mode, available on the Coding and Business plans, is built for this kind of work: explanations of code, bug fixes from a pasted error, and refactoring. A few features fit the workflow above particularly well.

• Projects. Create one project per codebase and keep the map, glossary and your notes as project files and instructions, so every new chat starts with context instead of from zero.
• File uploads. Upload source files or a ZIP of the relevant folders and ask targeted questions about them. Paid plans support uploads up to the size shown on the pricing page.
• Connectors. The developer connectors let Mio read a GitHub repository when you ask, which saves copying files by hand.
• Code runner. On plans with the code execution sandbox, Mio can run small Python or Node snippets to check how a piece of logic behaves with sample input.
• Code review. Once you start changing things, the approach in our guide to code review with AI helps you catch mistakes before they are merged.

Keep your company’s rules in mind: if your employer does not allow source code to be shared with external tools, check before uploading anything. Ask Mio runs on EU servers and does not use your data to train models, but the decision about company code belongs to your company.

A two-day plan for a new codebase

Day 1, morning: map the project from its structure, README and dependencies. Verify the entry point and two other claims. Day 1, afternoon: trace one complete user-facing path and draw it as a diagram. Start the glossary.

Day 2, morning: trace a second, different path, such as a background job or an admin function. Read the history of the most confusing files. Day 2, afternoon: explain both paths back to the assistant, fix the gaps, and write small tests around the logic you will touch first.

By the end you will not know everything, but you will know where things live, how requests flow, and what the team’s words mean. That is enough to make a first safe change.

Frequently Asked Questions

Can AI explain an entire codebase at once?

For a small project, roughly a handful of files, yes. For anything larger, a whole-repository summary is usually shallow and hard to verify. You get better results by asking for a map first, then working through one real path at a time and asking narrow questions such as where a particular calculation happens or which files call a particular function.

What should I give the AI first when I start on a new project?

Give it the folder structure limited to two or three levels, the README, the dependency file and any deployment configuration. Those few files reveal the type of application, the framework, the main entry points and where business logic probably lives. Ask the assistant to mark which parts of its answer are guesses so you know what to verify first.

Is it safe to upload my company’s source code to an AI assistant?

That depends on your company’s policy, not only on the tool. Check the rules before uploading anything. When choosing a tool, look at where data is stored, whether it is used for training and whether you can delete it. Ask Mio stores data on EU servers, does not train models on your chats and lets you delete your data at any time.

How do I know if the AI’s explanation of the code is correct?

Verify a few concrete claims yourself: open the file it called the entry point, search for usages of a function it described, or write a small test for a calculation it explained. Ask the assistant to say explicitly when it is describing code it has not seen. If basic claims are wrong, correct them and ask for a revised explanation.

Which Ask Mio plan do I need for code understanding?

The free plan covers Chat and Write, which is enough for explaining short snippets you paste in. For full Code mode, larger file uploads and the code execution sandbox, you need the Coding plan or the Business plan. Check the pricing page for the current points, upload limits and prices before choosing.

The Bottom Line

To understand a codebase with AI, change the order in which you read: map the project first, follow one real path at a time, decode the team’s vocabulary and use version history for the “why”. Verify the assistant’s claims as you go and write small tests before changing anything important. The AI speeds up orientation, but your checks make the understanding reliable. You can start with short snippets on the free plan, or compare the Coding and Business plans on the Ask Mio pricing page.