Case studies from real users
We’ve been amazed by the range of uses that people are finding for NotebookLM. Because the product was developed in close partnership with authors, students and educators, we’ve seen many early adopters integrate it into research and writing workflows. Best-selling author Walter Isaacson has been working with NotebookLM to analyze Marie Curie’s journals for research on his next book. We’ve seen similar enthusiasm from documentary and podcast researchers who need to sift through complex archives to generate scripts or story ideas. But the combination of Gemini 1.5 Pro’s advanced reasoning abilities and NotebookLM’s source-grounding architecture has unlocked many other potential applications:
- In local governance, Palm Bay resident Thomas Gaume created a hyperlocal newsletter, aggregating city ordinances, land use data, zoning codes and council meeting minutes. NotebookLM empowered him to be a “one-person newsroom and publisher.”
- NotebookLM’s ability to summarize and adapt interview transcripts is helping users identify patterns and themes in raw transcripts, saving hours of manual analysis. For example, consultant Victor Adefuye uses NotebookLM to analyze sales call transcripts for targeted training and coaching.
- Nonprofits have deployed NotebookLM to help them identify needs in underserved communities and organize information for grant proposals.
We’ve also noticed some unexpected and playful use cases with the help of our 14,000 member Discord community, including novelists and fan-fiction authors managing complex storylines using NotebookLM, and our favorite: role-playing game enthusiasts consulting detailed descriptions of fantasy worlds for games like Dungeons and Dragons.
Getting started
If you’re new to NotebookLM, getting started is easy: When you first access NotebookLM, you’ll create a notebook and upload documents for a specific project or deliverable. At that point you can read, take notes, ask questions, organize your ideas, or ask NotebookLM to create automatic overviews of all your sources — a study guide, for example, or a table of contents. And with NotebookLM, the sources you upload are not used to train the model.
Whether it’s being used to build imaginary worlds, write bestselling biographies, or help salespeople find new customers, NotebookLM has given U.S. users powerful tools for making connections and generating insights out of large collections of documents. We can’t wait to see what the rest of the world does with it.
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