NotebookLM is, in my view, one of the most powerful AI-driven tools currently available for researchers, educators, and students. I’ve tested countless AI tools over the past year, and as someone wrapping up a book on the use of AI in academic research, I don’t say this lightly, NotebookLM stands out, and the fact that it’s free makes it even more remarkable.

What sets NotebookLM apart is that it doesn’t behave like typical AI chatbots or generative models. It’s grounded entirely in the sources you upload. That means its responses are based on the actual content of your documents: papers, PDFs, YouTube URLs, lecture notes, or any other text you feed it.

For those of us working in academia, this is a major plus. The chances of hallucinations, those notorious AI-generated errors, are significantly reduced. I say “reduced” because no AI is flawless.

Now, you might be wondering how exactly NotebookLM can support your research. This post gives you a brief introduction. For a deeper dive, not just into NotebookLM but also a whole suite of tools that can supercharge your research workflow, stay tuned for my upcoming book.

Back to NotebookLM: it’s especially useful when you’re doing close readings for literature reviews or tackling intellectually demanding tasks. While you can technically use it to read just about anything, my focus is squarely on its value for academic work.

The way it works is simple and easy: you upload your documents to NotebookLM and then engage with them through a built-in chat interface. You can ask questions about the content, explore methodologies, extract quotes, summarize sections, and more. It does all of this seamlessly. One unique feature, something I’ll touch on later, is its audio overview, which adds a completely new layer to how we interact with research material.

NotebookLM Tips

Ready to start exploring NotebookLM?

Here are some helpful tips to ensure you’re using it effectively in your research.

First things first: you’ll need to sign in with your Google account. As I mentioned earlier, NotebookLM by Google is completely free, though there is a paid “Plus” version designed for teams or larger institutions. But don’t worry we won’t need that here.

Once logged in, the initial step is creating a notebook, think of it like a dedicated folder for your research. Give it a descriptive name. For example, if you’re researching climate change, you might call your notebook “Climate Change Sources” or something along those lines.

NotebookLM allows you to upload up to 50 documents per notebook, including various file types such as Google Docs, PDFs, Slides, and even YouTube URLs. However, based on my personal experience, I recommend limiting yourself to around 10 to 15 documents per notebook.

Beyond this number, NotebookLM can struggle a bit making its responses less precise. If your research involves a large body of literature, the best workaround I’ve found is creating multiple notebooks each containing no more than 15 documents.

After creating your notebook and uploading the relevant documents, you’ll see a page divided into three main columns (see screenshot below). On the left, you’ll find the sources you’ve uploaded, all automatically selected by default. NotebookLM immediately processes these documents and provides a summarized overview in the middle column. On the right side, you’ll notice additional features like Audio Overview, Study Guide, FAQ, Timeline, and Briefing Doc.

Now, you have two options. You can either keep all the papers selected and begin interacting with them collectively through the chatbox in the middle, or, and this is my preferred approach, you can deselect everything and focus on one paper at a time.

This way, you can ask detailed questions, generate FAQs, or create study guides specifically tailored to each paper. From experience, I’ve found that this method leads to deeper insights and a clearer understanding of each source.

One feature I particularly appreciate in NotebookLM is “Save to Note,” which lets you conveniently capture your entire chat as a note. Personally, once I’ve finished interacting with a particular paper, I save the entire conversation.

Later, when I revisit the papers, I simply review these notes, they neatly summarize the discussion and remind me of key points or questions I previously explored. I find this approach highly efficient for recalling essential information.

Occasionally, I even copy these notes from NotebookLM into my personal note-taking system (like Obsidian or Notion), organizing them further into folders corresponding to different sections or themes of my research.

Another helpful use of NotebookLM occurs during writing. Let’s say you remember a compelling idea from the literature you have read but can’t recall who said it. Instead of manually digging through papers, you can easily return to NotebookLM, select all relevant documents, and query the chatbot directly.

NotebookLM responds clearly providing citations along with the answers. This capability is invaluable, saving significant time when verifying sources during the writing process.

Now, about the Audio Overview feature I briefly mentioned earlier, this one isn’t as helpful for intellectually intensive tasks like literature reviews or detailed research reports, at least from my perspective.

Essentially, Audio Overview generates an audio-based discussion of your document, presented as a conversation between two AI hosts. You can even customize the discussion by specifying topics, points of focus, or intended audience.

It typically takes just a few minutes to create this “podcast,” and it sounds remarkably natural. I personally find it more useful for casual or supplemental reading, such as online articles or Substack posts from authors I follow. I’ll often generate audio overviews and listen to them while driving, exercising, or multitasking.

Final thoughts

Finally, as is always the case with any AI tool I recommend, it’s essential to be mindful of potential limitations and ethical considerations. AI-generated hallucinations, where the system produces inaccurate or completely fabricated information, remain possible.

Citations, page numbers, or quotes provided by AI should always be double-checked for accuracy. Additionally, uploading copyrighted material poses ethical issues, so you must exercise caution. NotebookLM claims explicitly that your uploaded documents aren’t used to train their models and can be deleted whenever you’re finished. Still, prudence and caution remain key practices in responsibly using such tools.

For those of you keen on discovering more ways AI can significantly enhance your research workflow, make sure to check out my upcoming book. It’s packed with practical tips, useful tools, and essential resources designed to help you harness AI effectively and boost your academic productivity.

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