How to prepare your repo for LLMs (and why it matters)

Large Language Models are only as good as the context you give them. Paste a messy, bloated codebase into Claude or ChatGPT, and you'll get confused, hallucinated answers. Paste a clean, focused one, and the AI becomes a genuinely useful pair-programmer.

In this guide, we'll walk through how to prepare any repository for LLM consumption — using Repo2Txt's built-in features plus a few manual touch-ups.

1. Let Repo2Txt do the heavy lifting

Repo2Txt automatically handles the most common cleanup tasks:

• Respects .gitignore — anything you've gitignored (build output, dependencies, secrets) is automatically excluded.
• Drops node_modules, target, .venv — dependency folders that bloat your context with thousands of irrelevant files.
• Skips lockfiles — package-lock.json, yarn.lock, Cargo.lock are repetitive and useless for LLMs.
• Detects binaries — images, videos, PDFs, compiled binaries are all skipped automatically.
• Detects minified files — minified JS/CSS bundles are caught by the long-line heuristic.

For most repos, the defaults are enough. Pick a folder, get a clean text file, paste it in.

2. Use the interactive file tree for fine-tuning

Sometimes you want more control. Maybe you're working on a specific feature and only want to include files related to it. That's where the interactive file tree comes in.

After converting, expand the file tree and uncheck anything that's not relevant to your question. The token count updates live, so you can see exactly how much context you're saving.

Pro tip: If you're asking about a bug in the auth flow, uncheck everything except your auth-related files. Smaller context = faster, more focused responses.

3. Watch the context limit gauge

Different AI models have different context windows. Claude Opus 4.5 has 200K tokens, Gemini 2.5 Pro has 1M, and GPT-5 nano has just 272K. The Context Limit Gauge shows you exactly how much of your selected model's window your code occupies.

Aim for under 70% usage — that leaves room for the model to actually respond. If you're over, exclude more files or pick a model with a larger window.

4. Check the codebase health scorecard

Before pasting your code, glance at the health scorecard. If your "Code Cleanliness" score is low because of 50 TODO comments, the AI will probably suggest you clean those up instead of answering your actual question.

It's not about gaming the score — it's about being aware of what the AI will see first.

5. Pick the right output format

Repo2Txt offers four formats:

• Plain Text — best for terminals and simple chatboxes that don't render markdown.
• Markdown — best for GitHub, Gemini, ChatGPT, and most chat UIs.
• JSON — best for programmatic pipelines, RAG ingestion, or scripts.
• LLM Chat — a natural-language message format optimized for Claude/GPT/Gemini chat windows.

For most casual use, Markdown is the safe default. If you're building a pipeline, JSON is the way.

Conclusion

Preparing your repo for an LLM isn't hard — Repo2Txt handles 90% of it automatically. The remaining 10% (selecting relevant files, watching the context limit, picking the right format) is where you can squeeze out better AI responses.

Try it on your next project and see the difference. Your AI assistant will thank you.