How Do You Connect AI to Your Notes?

What are the simple ways to give AI your notes?

You can give an AI context without setting up a connected knowledge base. The simplest methods are to paste relevant text into a chat or attach files if the client supports attachments. For a larger searchable collection, retrieval-augmented generation, or RAG, uses tools to break notes into pieces, represent them for search, and retrieve relevant material.

Can you paste notes into a chat?

Yes. Copy the part of a note that matters and include it in your prompt. This is a quick option when you have a small amount of text and want an answer based on exactly what you supplied.

The main limitation is that you must locate and provide the useful material each time. If notes are spread across many documents, selecting the right passages can take work. A pasted excerpt also gives the AI only the context included in that conversation.

• Fits a short note or a focused question.
• You choose exactly which text the AI sees.
• You must find and paste the relevant material again when needed.

Can you export notes to files and attach them?

If your AI client can accept attachments, you can export notes to files and attach the documents you want it to read. This is useful when a note is easier to share as a complete document than as copied text.

This route depends on the client supporting the file types and attachments you need. You also need to choose the right files for each request. A folder of attachments does not automatically give you a searchable knowledge base; the AI can only use files it can access in that interaction.

• Fits questions about a known set of documents.
• Keeps more source context together than a short pasted excerpt.
• Requires compatible attachments and selecting the files for the task.

What is RAG, in plain words?

RAG stands for retrieval-augmented generation. In plain words, it is a way for a system to find useful parts of a collection and give them to an AI when it answers. A common setup splits notes into smaller sections, creates representations called embeddings, and searches those representations to retrieve relevant text.

This can help when the collection is larger than what you would want to paste into a chat. RAG adds a preparation and retrieval system, though. The quality of an answer still depends on the material available and whether retrieval finds the relevant evidence.

The phrase RAG describes a general approach, not one specific service. If you use a RAG system, understand what it indexes, how you add or update notes, and how you inspect the sources behind answers.

• Fits a collection that you want to search repeatedly.
• Retrieves relevant material instead of requiring you to paste everything.
• Needs setup to prepare notes and retrieve the right passages.

What are the limits of these general approaches?

Pasting text is fast, but it asks you to select and supply the context. Attachments can preserve whole documents, but each client has its own file support and you still need to choose what to attach. RAG can search a larger set, but it depends on a retrieval system and an index of your notes.

How does an MCP connection give AI access to notes?

MCP is a way for an AI client to connect to tools from another service. Instead of manually pasting every source, a connected client can call tools to search a knowledge base, read a saved item, or ask a question across saved reading.

KnowMan's MCP server works with Claude Desktop, Claude Code, Codex, Cursor, and any MCP client. KnowMan turns saved articles, newsletters, and feeds into a personal knowledge base, reads the full text of saved items, and lets articles tag themselves. Answers come from saved reading, not the internet, and cite saved sources.

To let an agent use the library, create an agent key in Settings and choose its permissions. Agents can search, read, ask, see recent saves, and save links when allowed. They can read memories, which keep your decisions and reasons next to the reading behind them, but cannot write memories.

1. In KnowMan Settings, create an agent key and choose only the permissions you want to give the agent.
2. Paste one configuration snippet into your MCP client.
3. Ask the connected agent a question about something in your saved reading.
4. Review the citations and open a saved source when you want to check the evidence.

Which way of connecting AI to notes fits your situation?

Use the least complicated method that gives the AI the material and repeatability you need. Pasting works for a focused question about a small excerpt. Attaching files works when you know which documents matter and the client supports them. RAG is a general option for retrieving relevant text from a larger indexed collection.

An MCP-connected knowledge base fits when you want a client to search and read saved material as you work, without repeatedly assembling the context yourself. It is also useful when citations matter and you want answers grounded in your saved sources.

You can start without signing up for any particular service: paste a note or attach a file in a client that supports it. If you want a durable, searchable setup with the verified clients listed above, connect KnowMan through MCP and try a question about a saved item.

• One excerpt, one quick question: paste the note.
• A known set of documents: attach files if your client supports them.
• Repeated search across a larger collection: consider RAG or an MCP-connected knowledge base.
• Saved reading with citations in a verified MCP client: connect KnowMan.