How to Understand an Unfamiliar Codebase with AI

Why does AI help with unfamiliar code?

The slow part of joining a codebase is not reading code line by line. It is finding which files matter, how pieces connect and why things were done a certain way. AI assistants are good at the first two: searching, summarizing and connecting call sites across many files quickly.

They are weaker at the third. The reasons behind design decisions often live in pull request discussions, issue threads and people’s heads, not in the code. An assistant may invent a plausible reason, so keep asking where its claims come from.

Used well, AI shortens the time to a correct mental model. Used carelessly, it gives you a confident, wrong one faster.

The goal is not to have the AI understand the code for you. It is to spend your own attention on the parts that matter instead of on finding them.

Which tools should you use?

The biggest difference between tools is how much of the repository they can see and whether they can run commands. Agentic coding tools that search and read files on demand usually beat pasting snippets into a chat window.

How do you explore a codebase step by step?

• Clone the repo and get it running locally first, even roughly, so you can test your understanding against real behavior.
• Ask the assistant for a map: top-level directories, entry points, main modules, build and test commands, and external services it talks to.
• Pick one concrete flow, such as a single API request or a CLI command, and ask the assistant to trace it from entry point to database or output, naming files and functions.
• Open each file it names and confirm the path yourself. Correct the assistant when it is wrong; that sharpens later answers.
• Ask for the core data models and how they relate, then find where each is created, changed and deleted.
• Run the test suite and ask which tests cover the flow you traced. Tests are executable documentation.
• Make a small, safe change, such as adding a log line or a field, and see what breaks.
• Write down what you learned in a short architecture note, and keep it in the repo or your own notes.

Which questions get useful answers?

Vague questions get vague summaries. Questions tied to concrete code, behavior or change get answers you can verify.

• Where is the entry point for the upload endpoint, and which functions does it call, in order?
• Which modules read or write the invoices table? List files and functions.
• What happens if the payment provider times out during checkout? Point me to the code.
• Which configuration values change behavior in production, and where are they read?
• If I add a field to the user model, which files must change, including migrations, validation and tests?
• Which parts of this code look unused or duplicated? Show the evidence.
• What does git history say about why this module was rewritten? Summarize the relevant commits.

How do you verify what the AI tells you?

Ask for file paths and function names with every claim, and open them. If the assistant cannot point to code, treat the claim as a guess.

Confirm behavior by running it. Add a breakpoint or log line, trigger the flow and see whether the call order matches what you were told. A quick experiment beats a long explanation.

Cross-check with history. Commit messages, blame and linked pull requests often explain why code looks strange. Assistants that can run git commands can summarize this for you, but read the original messages for anything important.

How do you handle large repositories and monorepos?

No assistant holds a large monorepo in context at once. Scope your questions to one package or service, and start new sessions when you switch areas so old context does not confuse new answers.

Tools differ in how they cope. Some build a compact repository map from symbols, as Aider does, while others search on demand with grep and file reads. Either way, give the assistant a starting directory and the names of the components you care about.

Write your findings into a project instruction file, such as CLAUDE.md or an equivalent for your tool, with build commands, key directories and conventions. Future sessions, and future teammates, start from that instead of rediscovering it.

How do you turn exploration into documentation?

The notes you write while learning are valuable to the next person, and AI makes producing them cheap. After tracing a flow, ask the assistant to draft a short explanation with file paths, then edit it until it matches what you verified.

Keep documentation close to the code: a docs folder, module-level comments or an architecture decision record for choices you uncovered. Diagrams generated as text, such as Mermaid, can be versioned and updated with the code.

Mark what is verified and what is inferred. A note that says “confirmed by running the import job” is worth more than one that says “the assistant thinks this is dead code.” Reviewers can then trust the verified parts and check the rest.

Share early. A teammate who knows the codebase can correct a wrong assumption in minutes, and a merged documentation pull request is also a low-risk first contribution to an open-source project.

Where does it break? Common mistakes

• Accepting architecture summaries without opening a single file.
• Pasting fragments into a chat and asking about behavior that depends on code the model never saw.
• Letting an agent refactor before you understand the code, turning exploration into an unreviewed rewrite.
• Giving an agent broad shell permissions in a repo with production credentials in environment files.
• Uploading proprietary code to a tool your company has not approved. Check the data policy first.
• Skipping the humans. A short conversation with a maintainer can replace hours of guessing about intent.

How should you use AI to evaluate an open-source project?

When deciding whether to adopt a project, the same workflow becomes an evaluation. Ask the assistant to find how configuration, authentication, error handling and storage work, and to flag anything surprising, such as hard-coded secrets, disabled checks or network calls you did not expect.

Ask it to estimate what it would take to add a feature you need, with the list of files involved. If the answer spans the whole codebase, the project may be harder to extend than its README suggests.

Combine this with licence and health checks before committing. RepoLoot’s catalog summarizes what each project does and its difficulty, which is a useful first filter before you spend an afternoon inside the code.

Frequently asked questions

Which AI tool is best for understanding a codebase?

Tools that can search and read the repository themselves, such as terminal coding agents or IDE assistants with project indexing, usually beat pasting snippets into a chat. The best choice depends on your editor, repository size and your company’s data policy. Try two on the same flow and compare accuracy.

Can I paste a whole repository into a chat model?

For small repositories, tools like Repomix or gitingest can pack the code into one file that fits a large context window. For bigger codebases it will not fit or will dilute attention. Scoped questions with a tool that reads files on demand work better as projects grow.

How do I stop the AI from hallucinating about the code?

Require file paths and function names for every claim, open them, and confirm behavior by running the code. Keep questions concrete and scoped. Start fresh sessions when switching areas, and correct wrong statements explicitly so the assistant does not build on them later in the conversation.

Is it safe to use AI tools on proprietary code?

It depends on the tool’s data handling and your company’s policy. Check whether code is retained or used for training, which providers see it and whether an approved enterprise plan or local model is required. Keep secrets out of the repository and restrict agent permissions regardless.