How to Feed Your Entire Codebase to an LLM

Sometimes the thing you want to ask about is not one file, it is the whole project: how the pieces fit, where a bug is hiding, whether an approach holds across the codebase. For that, the model needs to see the repo, not a snippet of it.

This guide covers how to feed your entire codebase to an LLM like ChatGPT or Claude: why pasting the raw folder rarely works, how to cut it down to what matters, and how to pull a repo straight from a URL. There is a spot to try it on your own repo partway down.

Why pasting a raw repo fails

When you try to give ChatGPT or Claude your whole codebase by selecting the project folder and pasting everything, two things go wrong.

The first is noise. A working checkout is mostly not your code: node_modules, build output, lock files, the .git directory, vendored dependencies, generated assets. Paste all of it and the signal you care about is buried under the parts you do not.

The second is size. Every model has a ceiling on how much text it takes at once, and an installed repo runs past it easily. Fitting a large codebase into a context window is really a filtering problem, not a copy-paste one: the dependencies alone are usually larger than a chat box will accept.

my-app/
node_modules/     # thousands of files
dist/             # build output
.git/
package-lock.json
src/              # the part you want
README.md

my-app/
src/
package.json
README.md

Those figures are illustrative, but the shape holds on almost any repo: the dependencies and build output are the bulk of the bytes and almost never what you want the model to read.

Cut the noise first

The fix is to send a curated snapshot: your source, your config, your docs, and nothing that a fresh install or a build would regenerate.

FileConcat does this by default. It leaves out lock files, build output, binaries, and the usual dependency directories, and it respects your .gitignore on top of that. The bundle starts as the signal, and you add back anything you actually want.

Bring in a repo by URL

You do not need the code checked out locally. Paste a public repository URL and FileConcat pulls the files, filters them the same way, and hands back one bundle. For most projects this is the best way to share a codebase with ChatGPT or Claude, and the fastest way to convert a GitHub repo to text for either one.

1. 1

   Paste the repository URL

   A public GitHub, GitLab, or Bitbucket URL is enough. FileConcat resolves the default branch and lists the files. See importing from GitHub.

2. 2

   Let the noise fall away

   The same default filter runs on the fetched tree, so dependencies, build output, and lock files never make it into the bundle.

3. 3

   Copy the bundle

   You get one structured document with every file labeled by its path, plus a running token count so you know it fits before you paste it.

Try it on your own repo

Drop a project folder below, or open the full tool to paste a repository URL. It runs in your browser, there is no account, and nothing is uploaded to us.

When a paste is the wrong tool

Bundling a codebase into one prompt is the right move when you want a model to reason over a snapshot: review an approach, explain a module, find where something is defined. It is not the right move for everything.

If the repo is too large to fit even after filtering, or you want the model to edit across many files and iterate, a single paste is the wrong shape. An agentic coding tool that reads files on demand, or a retrieval setup that fetches only the relevant parts, will serve you better there. Command-line packers like Repomix and gitingest cover the same bundle-a-git-repo need in the terminal; FileConcat's difference is that it runs in the browser, handles documents as well as code, and shows the token cost as you build.

Mind the token budget

Because the ceiling is real, it helps to know the size of a bundle before you paste it. FileConcat counts tokens live as you include and exclude files, so you can trim until it fits instead of finding out when the chat box refuses it.