NotebookLM FAQs

Question 1

Can I organize my documents into different searchable collections?

Accepted Answer

Yes, NotebookLM supports organizing your documents into multiple searchable collections. This feature allows you to categorise your information by topic (e.g., 'research', 'work', 'school'), making it easier to manage and query specific sets of documents within Claude Desktop.

Question 2

How does NotebookLM ensure my data privacy and prevent hallucinations?

Accepted Answer

NotebookLM guarantees data privacy by storing all your documents and vector embeddings 100% locally on your machine using ChromaDB – no data ever leaves your computer. It prevents hallucinations by strictly deriving all answers from the content of your indexed documents, providing verifiable and accurate information.

Question 3

How do I get started with NotebookLM for Claude Desktop?

Accepted Answer

To get started, you'll need to clone the MCP-RAG repository, install dependencies (Node.js and Python for ChromaDB), start the ChromaDB server, and then use the command-line interface (CLI) to add your documents. Finally, configure Claude Desktop to recognize the MCP-RAG server, and you can begin asking questions based on your private knowledge.

Question 4

What types of documents can I index with NotebookLM?

Accepted Answer

You can index a wide array of document formats, including PDF, DOCX, PPTX, XLSX, HWP (Korean word processor format), TXT, and MD files. This versatility allows you to consolidate various types of information into your personal knowledge base.

Question 5

What is NotebookLM for Claude Desktop (MCP-RAG)?

Accepted Answer

NotebookLM for Claude Desktop (MCP-RAG) is a personal, 100% local Retrieval-Augmented Generation (RAG) server designed to integrate with Claude Desktop. It allows you to index your private documents and query them using natural language, ensuring answers are derived strictly from your data, free from AI hallucinations.

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