What Is an LLM Wiki? A Simple Guide to Karpathy's Personal Knowledge System

AI Summary: An LLM wiki turns your personal notes into a conversational knowledge base. Instead of clicking through folders and apps to find something you wrote down, you simply ask a language model a question and it finds the answer within your own markdown files. This article explains the core idea behind an LLM wiki, how it differs from traditional note-taking apps, why markdown is the ideal format, and how to set one up using tools like Obsidian and Claude Code. It also covers best practices — writing one idea per note, starting each note with a summary, and using consistent terminology — along with the limitations to be aware of as your collection grows.

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Instead of digging through apps, folders, and all those random places where we save things, an LLM wiki lets you simply ask a language model a question, and it finds the answer within your own notes. Karpathy's original LLM wiki gist points out that with a little organization, your notes can become a sort of "wiki" for a language model to use. And it doesn't need a complicated app; it's just plain Markdown files on your computer. Basically, an LLM wiki makes your notes searchable through conversation, instead of all that clicking and browsing.

The Central Idea

The central idea is pretty straightforward. Most of us spread our knowledge across documents, note apps, bookmarks, and loose files, which predictably makes finding anything later a huge headache.

An LLM wiki solves this by keeping everything in one spot, saving notes as uncomplicated text (markdown) and then letting the language model actually read and answer questions based on those notes.

Instead of having to remember where you put something, you could ask, "What notes do I have on marketing strategy?" or "Give me a summary of my research into pricing models". The model then goes through your files and provides a concise answer, and importantly, that answer is based on your information, not what's on the internet. You shift from finding your knowledge to asking it.

How It Differs From Normal Note-Taking Apps

Most note-taking apps are designed for us to do the searching, the scrolling, and the clicking through folders. An LLM wiki is built so the model can read for you. With traditional notes, you're manually searching, relying on your memory, and opening files one at a time.

An LLM wiki, though, means you ask in normal language, the model does all the reading, and you get a single, neatly organized answer. This even alters how you take notes: you aren't just writing for your eyes anymore, but for the model as well.

Why Markdown Matters

Markdown is the basis for all this. It's a simple way to format text, using things like # for headings and - for lists. It's simple and will work anywhere — markdown files are just text files, and won't lock you into a specific program. Language models have been trained on tons of markdown, so they naturally understand headings, lists, and structure.

In fact, markdown almost forces you to be a better note-taker. You'll use headings, summaries, and distinct sections, making your notes easier for both you and the model to understand. Plus, you aren't dependent on any one platform; your notes remain yours indefinitely.

How The System Works

The system itself is surprisingly easy and doesn't need a huge amount of configuring. It's made up of three main parts:

A folder of notes — your complete knowledge base, full of markdown files with your thoughts, research, and information.
A consistent structure — each note ideally has a Title, a brief Summary, Tags, and then the Main Content. This helps the model swiftly understand the file's purpose.
A language model interface — such as Claude Code, which allows the model to read files directly from your computer. You open the tool, ask your question, and it scans your notes to provide a response.

How To Set Up A Basic LLM Wiki

You can have a basic one running in minutes, with a markdown editor and a model interface. First, make a folder — perhaps called wiki — and inside that, create folders for projects, research, notes, and a kind of holding pen called an inbox.

Choose A Markdown Editor

A markdown editor is what you need, and Obsidian is excellent; it's a really good way to manage and look at your notes, yet the files themselves live on your computer.

Create A Consistent Note Format

Decide on a format for each note, and stick to it. Something like:

A main title using a hash (# Topic Title)
A one-sentence summary
Tags (#topic #idea)
The content itself (## Content)
Related links (## Related)

This consistent look makes it really quick for the AI to understand what you've got.

Add Notes Gradually

Start adding notes bit by bit, the ones you refer to often. Don't try to dump everything in at once.

Connect A Model

Link up a model. Claude Code is a good choice, which will allow you to directly ask questions of your files. You'll be able to ask things like "What are my notes on content writing?" or "Summarize all my SEO research," and the model will read through your files and give you the answers.

Best Practices For Better Results

To get the best from your LLM wiki, concentrate on how it's arranged and how clearly you write things.

Start Each Note With A Summary

Each note should begin with a single line summary, giving the model a quick way to decide if it's useful for your question.

Keep One Idea Per Note

Don't cram lots of different subjects into one note; stick to one idea per file.

Use Consistent Wording

Be consistent with the words you use for the same thing. It improves the accuracy of the results when you ask questions.

Link Related Notes

Link notes together, building a web of knowledge that the model can explore.

Use An Inbox Folder

Use that inbox folder for quick, rough notes that you can sort out later.

When RAG Becomes Useful

As your collection of notes gets bigger, the model might take a while to locate the right info. This is where Retrieval-Augmented Generation (RAG) comes in. It first uses a search to find the relevant bits, then asks the model to read them. For a small, personal wiki, simply reading the files is usually enough. But RAG makes things faster and more accurate for larger systems.

Benefits Of An LLM Wiki

You get to your information much faster and get answers directly.
The model is using your notes, not just general stuff on the internet.
You have complete control over your data; it all stays on your computer, and there's no vendor lock-in.
Setting it up is easy; there are no tricky tools or databases needed, and it can grow as you do.

Limitations To Keep In Mind

It's a strong system, but an LLM wiki does have some boundaries. It relies on the quality of your notes. Very large collections of notes may need more sophisticated searching. And it does require a little initial set-up (the folder, markdown, and access to the model). However, for personal use, it's one of the simplest and most effective ways of doing things.

Why an LLM Wiki Is a Smarter Way to Manage Knowledge

An LLM wiki isn't a program or an app; it's a way of arranging your knowledge so the model can read and make use of it. The core idea is this: markdown for your notes, a structure for those notes, and then a model to answer your questions.

This cuts out the difficulty of managing information; you don't need to remember where you put things, you just ask. If you're diligent and consistent with your notes, the system will become more and more valuable as time goes on, growing alongside you and improving with use. For anyone who deals with lots of information every day, this is a really useful way of turning a jumble of notes into something both efficient and powerful.

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