Getting Up to Speed on an Unfamiliar Codebase With LLMs in November 2026: Start From the Entry Points, Make the Model Cite File and Line, and Confirm Every Claim by Running the Code

Joining a new team, inheriting a side project, or picking up an open-source library you want to patch all start the same way: a repository full of code you did not write and a vague sense that you should understand it by Friday. LLMs are a real help here. They read fast, they know the common frameworks, and they will happily explain a 400-line file in plain English. They will also confidently describe functions that do not exist, guess at how a request flows through the system, and fill gaps with what "usually" happens in projects like yours. This guide shows a workflow that uses the model to build a mental map quickly while keeping every claim tied to real files you can check.

A 1908 map of the London Underground. Like a new codebase, a network is easier to learn one line at a time, starting from where trips begin. Image: Tube map 1908 by an unknown author, public domain, via Wikimedia Commons.

Why codebase explanations go wrong

When a model explains code it has not fully seen, it pattern-matches. If your repo looks like a typical Django or Express app, the model will describe a typical Django or Express app, including middleware, settings, and folders you may not have. A few common failure modes:

• Invented structure. It names a services/ layer or an AuthMiddleware class because that is what similar projects contain.
• Stale knowledge. It describes how a framework worked two major versions ago, while your project uses the current routing or config style.
• Missing the weird parts. The custom build step, the monkey-patched library, or the environment variable that changes behavior in production are exactly what a generic summary skips, and they are usually what bites new people.
• Partial context. If you pasted three files, the model may still answer as if it saw the whole repo.

The fix is not to stop asking. It is to feed the model real material, ask narrow questions, and require answers you can verify.

Step 1: Give the model a real map, not just a repo name

Start by collecting facts the model cannot guess. Generate a directory tree that skips noise:

git ls-files | grep -vE '(^|/)(node_modules|vendor|dist|build)/' | head -300
# or, if installed:
tree -L 3 -I 'node_modules|dist|build|.git|vendor'

Then gather the files that describe how the project is built and run: README, package.json, pyproject.toml, go.mod, Makefile, Dockerfile, docker-compose.yml, and the CI workflow files. These tell you the language versions, the frameworks, the entry commands, and what the team actually tests.

Paste those into a single conversation (or point your coding assistant at them if it has repository access) with a prompt like this:

Here is the file list and build/config files for a repository I am new to.
Using ONLY what is shown, tell me:
1. The language(s), framework(s), and versions.
2. The likely entry points (processes that start: servers, workers, CLIs, cron jobs).
3. How to install, run, and test it locally.
For every answer, name the file that supports it. If something is not shown, say "not shown" instead of guessing.

The "name the file" and "say not shown" rules do most of the work. They turn a fluent summary into a checklist you can verify in a few minutes.

Step 2: Start from the entry points and follow one request

Reading a codebase alphabetically is slow. Reading it the way the program runs is fast. Pick one entry point, such as the web server's startup file, and one concrete action a user takes, such as "submit the signup form." Then trace that single path end to end.

Paste the entry file and ask:

This is the server entry point. List, in order, what happens at startup:
config loading, database connections, route registration, background jobs.
Quote the line or function name for each step.
Then tell me which file I should open next to find the handler for POST /signup.

Open the file it points to, paste it, and repeat. After four or five hops you will have a chain like: route definition, request validation, service function, database query, email job. Write that chain down in a scratch file as you go, with file paths. It becomes your first piece of real documentation.

If you use an editor-integrated assistant that can search the repository itself, the same rule applies: ask it to show the search results or open the file, not just to summarize from memory.

Step 3: Make every claim cite a file and line

The single most useful habit is requiring citations. Add this to any explanation request:

For each statement, include the file path and the function or line it comes from.
If you are inferring rather than reading, label it "inference".

Then spot-check. Open two or three of the cited locations. If a citation points to code that does not say what the model claimed, treat the rest of the answer with suspicion and re-ask with the actual file pasted. Labeled inferences are fine. They are often right, and they tell you exactly where to look next.

In the myth, Ariadne's thread let Theseus find his way through the labyrinth. File-and-line citations do the same job for you in an unfamiliar repo. Image: Theseus and Ariadne by Angelica Kauffman, public domain, via Wikimedia Commons.

Step 4: Confirm behavior by running the code

Reading tells you what code says. Running it tells you what it does. Once you have a theory about a path, test it:

• Run the test suite first. If tests pass on a clean checkout, you have a safe baseline. If they fail, that is important information about the project, not something to fix on day one.
• Set a breakpoint or add a temporary log line in the handler the model identified, then trigger the action. If execution never reaches it, the model's map is wrong somewhere upstream.
• Read the tests for the area you care about. Tests are executable documentation. Ask the model to explain what a test file proves, citing each test name, and you often learn the intended behavior faster than from the implementation.
• Check git history for context. git log --follow -p path/to/file and git blame show why odd code exists. Pasting a confusing commit diff and asking "what problem was this change solving?" is a good use of the model, as long as you read the commit message and linked issue yourself too.

Step 5: Ask about the risky parts on purpose

Generic summaries skip the things that cause outages. Ask directly, one area at a time, with the relevant files pasted:

• "Where does this app read environment variables or config, and which ones change behavior between development and production?"
• "List every place this code writes to the database, sends email, charges money, or calls an external API."
• "What runs on a schedule or in a background worker, and how is it triggered?"
• "Where are errors caught and swallowed without being logged or re-raised?"

Each answer should come back with file paths. Verify the list with a plain text search, for example grep -rn "os.environ\|process.env" --include=*.py --include=*.ts ., because a model working from partial context will miss some.

Step 6: Write it down and keep it honest

By the end of a few sessions you will have a scratch file with entry points, one or two traced request paths, the risky side effects, and the commands to build and test. Turn that into a short onboarding note, ideally committed to the repo as docs/architecture.md or added to the README. A good note answers:

1. How do I run it, test it, and deploy it?
2. What are the main processes, and where does each start?
3. How does one typical request flow through the system, with file paths?
4. What has side effects, and what config changes behavior?
5. What surprised me as a newcomer?

You can ask the model to draft this from your scratch notes, but edit it yourself and remove anything you did not personally confirm. Ask a teammate who knows the code to review it. The last item is the most valuable one, because it captures what long-time maintainers no longer notice.

What not to paste

Before sending code to any hosted model, check your employer's or project's policy. Never paste .env files, credentials, private keys, customer data, or production database dumps. If a config file contains secrets, replace the values with placeholders like REDACTED before sharing it. Exploring structure almost never requires the real values.

A realistic first-week plan

• Day 1: Build and run the project and its tests. Use the model to explain the build files, with citations.
• Day 2: Trace one user action from entry point to database, verifying each hop with a breakpoint or log line.
• Day 3: Map side effects, background jobs, and config, checking the model's lists with grep.
• Day 4: Read the tests for the area you will work in, and make a tiny, safe change such as improving an error message.
• Day 5: Write the onboarding note and get it reviewed.

The short version

Use the LLM as a fast reader and guide, not as the source of truth. Give it real build files and real source, follow one path through the program instead of reading everything, require a file and line for every claim, and confirm what matters by running the code. You will understand a new codebase faster, and what you learn will actually be true for this repository rather than for some average project that looks like it.