Wish you had a free AI assistant to analyze documents and instantly provide summaries, key insights, and answers? Google NotebookLM does that and more. This powerful tool streamlines research, helping you find the most relevant information with ease. Whether tackling complex topics or a simple Wikipedia page, NotebookLM helps you work smarter, not harder.
Knowledge You’ll Gain
- How NotebookLM turns your document(s) into a personalized AI research assistant.
- The difference between “grounded” AI and traditional large language models.
- Guidance on using NotebookLM’s key features.
- Tips for maximizing your efficiency with NotebookLM.
- Honest insights into NotebookLM’s limitations.
- Real-world examples of how NotebookLM can transform your research process.
Grounded AI: A NotebookLM Difference
In the world of AI, not all knowledge is created equal. Most large language models (LLM) are based on massive datasets. Think of various bots doing a large crawl of the Internet and analyzing articles from this website, Wikipedia pages, online forums, and countless other sources.
This size exposes the models to a large corpus of data from which they produce answers. Behind-the-scenes algorithms compute word relationships, proximity, and probability to get to the next word prediction.
Despite all this data, LLMs can provide wrong answers and yet sound convincing. Other times, the answers are flat-out wrong and even comical if they don’t understand humor. These mistakes are referred to as “hallucinations”. I briefly mentioned these in my tutorial on reading research papers with AI prompts.
However, by adding constraints, you impact relevancy and creativity. Google’s NotebookLM takes this approach and relies on the data you provide. In other words, a smaller controlled dataset can produce more accurate answers. This is essential when doing research or comprehending subject material. The answers are “grounded” in the data you supplied.
This means the AI’s knowledge and responses are based solely on user-provided documents, unlike other models trained on massive datasets. To give an example, I asked NotebookLM a question regarding Starbucks. Although the company was mentioned in my sources, nothing relates to my question. Instead of guessing, NotebookLM indicates it didn’t find relevant information.
NotebookLM Basics: Access & Data Privacy
Currently, the service is “experimental”. If you’re trying to learn or teach a subject, I wouldn’t let this deter you. The benefits far outweigh the limitations and quirks. This is the stage where Google tries to flush out user issues.
One concern people have regarding AI products is the use of their data. The good news is your uploads are not used to train their AI models. However, if you run into issues or provide feedback, human reviewers may see your queries and responses. So, I’d probably refrain from uploading confidential information like draft patent applications. As a side note, NotebookLM is good at analyzing existing patents.
Your AI-Powered Virtual Research Assistant
I like to think of NotebookLM as a combination of a research tool and an obedient research assistant. It excels in summarization, organization, questions, and insights. This is why I think it is great for learning or taking notes.
Once you add content to your notebook, it goes to work and starts its text analysis. You’re not required to add all your data at once. You can upload a single file if you wish. This is handy if you need to understand your homeowners’ association manual, an election ballot measure, or a Wikipedia page.
And like human researchers, the service may not save all your chats when you close the session. You need to do that part.
Analyzing Content: A Wikipedia Example
I think a good way to understand the service is to try it out with a single URL or Wikipedia page. For this article, I’ll use the Wikipedia coffee page. These page types tend to be long and have many contributors and references. I like Wikipedia and contribute to the foundation, but some pages make my eyes gloss over.
The upload process starts by adding a Source. A menu appears that offers many upload options. I added the Wikipedia URL.
NotebookLM adds the document to its Sources and starts its text analysis. The document will not be a picture-perfect representation of the web page. In this case, it adds numerous links for language translations and menu options. And at this moment, it won’t process images on the page except for captions.
In a short time, it provides the following information to get me started much like a research assistant.
Let’s look at some of the components.
[A] A Notebook guide with sections for FAQ, Study Guide, Table of Contents, and a Briefing doc.
[B] A summary of the selected source such as the Wikipedia page
[C] Some suggested questions
[D] A chat area where I can ask my questions.
All the above information was provided automatically based on the initial document. What’s nice about this approach is as I add more sources, I can toggle which resources to analyze. For example, I may want to build FAQs on all the information sources I upload, not just one.
Notebook Guide: Your Research Companion
The Notebook guide is a key part of the learning system. The feature is disabled on shared notebooks such as the ones made by Google to demonstrate the service.
After you upload your first source file, you’ll see the area. Across the top row, you have a series of Generate options. These include:
- FAQ
- Study Guide
- Table of Contents
- Timeline
- Briefing Doc
If you don’t see these options, you may be in “Chat” mode. If so, click the link labeled * Notebook guide. It should be to the right of the chat textbox.
Saved Responses
Anytime you use one of the Notebook guide buttons, you create a Saved Response. This is where the service creates a view-only card entity for you. This means when you close your notebook down, you won’t lose the info it generated.
These cards can be very useful, but also have some quirks regarding formatting and citations which I’ll mention later.
Generating Frequently Asked Questions (FAQs)
I don’t know why Google calls these “FAQ”s unless they have some record of people uploading this same page. Instead, I think of these as “starter questions”. All the same, they are a nice option for people starting to learn a subject.
In NotebookLM you just need to click the FAQ button. Based on your document(s), it will create a new “Saved response”.
When you create these Saved responses, you don’t get a note title (A) so I would rename them if you plan to keep them. In the future, I hope Google will not use an enumerated list, but label the question (B) with a Q. and the answer (C) with an A. Thankfully, the questions are bolded.
In the interim, you can copy the text and make a new note. I do this to add spacing to the questions and answers. Id’ suggest always adding a descriptive note title and saving it. You can always delete notes you don’t need.
As you can see the answers tend to be short and don’t include citations. I can take one of the generated FAQs and enter it into the chat box to get a more detailed answer with citations.
Creating a Study Guide
This feature will please people, such as educators, who plan to create content from the source documents. As before, you click the Study Guide link. The system will respond by providing you with another Saved response consisting of four sections:
- Quiz
- Answer key
- Essay suggestions
- Glossary
It’s important to note that this area isn’t interactive. At present, there isn’t any way to have the service ask you questions and evaluate your response. However, you could get creative with your questions, posit your theory, and see the response.
While this section is useful, I would copy the contents to another document because of the formatting. I found the questions to be more detailed than the ones from the FAQs.
The biggest issue I find is the Glossary. The system offers no formatting so it’s difficult to discern the terms.
An easy workaround is to ask the system to create a glossary based on your criteria. In the example below, I’ve asked for the terms to be bolded and added spacing. If this is an action you routinely use, I’d use something like TextExpander to save the prompt for future use.
Table of Contents (Document Guide)
This feature is a good starting point for comprehending a single document. It’s more than what I consider a table of contents, (like I have above) but more a descriptive outline. In the case of the Wikipedia page, you can see that the headings are similar and it has not processed navigational or language items. In addition, the service added a brief description to sections.
My quick guess without doing additional testing is Google uses the HTML markdown to determine headings and perhaps font size or font attributes. My suggestion is if you add your documents, use a good document structure.
Timeline & Cast of Characters
This is where Google pulls out key events based on dates. As you might expect, Wikipedia pages are full of such information. What I didn’t expect was the system to reference prominent characters too. Am I the only one right now thinking back to high school and manually plotting Shakespeare plays?
Questions, Answers, & Getting Insights
The above Notebook guide features are nice, but NotebookLM shines when you begin asking it questions. In this example, I used the chat area to ask a question about health benefits. Like many chat services, such as ChatGPT, it keeps track of your question (A).
A detailed answer appears below with bold terms and text. You’ll also see that the system provides citations as to where it found the answer. You simply need to hover over the citation number and a reference card pops up (C).
If I double-click the citation number, the source document (Wikipedia page) opens in the left pane. The system highlights the relevant passage so it is easy to find.
Conversely, if I highlight a different passage from the left it generates more suggested actions. As you might imagine, you can drill down pretty far based on your content.
Notes in NotebookLM
NotebookLM’s ability to highlight questions and suggest actions is impressive. But, there are times when you want to add notes. Maybe these are questions you wish to save or a reminder to yourself.
You can add your free-form notes or make notes from existing content. Any note you add is editable and includes a formatting bar. In contrast, notes saved from a Notebook Guide feature or a chat session are not.
Pinning a Question’s Answer (Saved Response)
One useful way to save notes is to “pin” them from the answers. Each answer area in the Chat section shows a large push pin in the top-right corner.
Clicking this icon will save the answer as a new Saved Response note. This is a useful feature, but I would get into the practice of renaming the note to reflect your original question. Otherwise, you lose the context.
While this feature is useful, the text loses some functionality. For example, you can’t click the citation numbers to jump to the relevant section in the source document.
Adding Your Notes (Written Note)
Another useful feature is adding free-form notes. Once you close the Chat area you can see a link to add notes. These are a bit different since you also get a formatting bar and can edit the text. I like to use these to create tasks. Once I’ve completed it, I’ll delete the note.
Limitations of NotebookLM
As with any experimental tool, NotebookLM is continually being refined and improved by the Google team. There is also a Discord group where you can provide feedback and bugs. While it’s still under development, the core functionality of summarizing, analyzing, and answering questions based on your documents is incredibly powerful.
Here are some things to consider:
- NotebookLM doesn’t currently support linking between notebooks.
- Must be 18 years or older. (sigh)
- You won’t receive reminders to save unsaved responses.
- Saved Responses don’t include clickable citations.
- Notes can’t be re-ordered and don’t include creation dates
Are you ready to experience a smarter way to research? Try NotebookLM for free by clicking the button below and see how it can help you work more efficiently. A simple Wikipedia page is a great place to start. And no, you don’t have to use my coffee page. I chose coffee as it seems everyone likes it.