#NotebookLM for digging into dense topics

One of the AI tools saving me the most time right now, whenever I need to get up to speed on a dense topic, is Google’s NotebookLM.

It’s what’s known as a RAG (Retrieval-Augmented Generation): an LLM that works from the specific sources you hand it.

Using it is simple. You upload files, URLs or videos, and the tool digests them in a few minutes. From then on, NotebookLM is a virtual expert on the subject. You can ask it for a summary of the key points, or drill into specific questions.

The killer feature: it can even generate a podcast (yes, you read that right), with two virtual “hosts” chatting about the topic for 10 to 20 minutes. I challenge you to tell they aren’t human.

What it does better than ChatGPT or any comparable general-purpose tool:

• It goes deep into the material you give it, with precise answers
• It cites the documents and passages behind each answer (in AI terms, that’s called grounding)

In practice, I find NotebookLM useful in several situations:

• Quickly summarizing very long or complex documents (the State of AI 2024 report, for instance)
• Reading research papers with far less effort (it makes a data scientist’s life easier)
• Comparing several sources on the same subject to get a clearer picture
• Some people even turn it into an FAQ chatbot, once they’ve loaded in all the relevant resources