15 Incredibly Useful Things You Didn’t Know NotebookLM Could Do

Generative AI may be both the most useful and the most mystifying tool of our modern-tech era. The problem—aside from all the endlessly documented issues around accuracy—is that generative AI generally seems to function in a DOS-like blank prompt form. The onus is squarely on you to figure out what to ask and how to put these saucy systems to use. That black-box feeling is especially apparent when you look at NotebookLM, an “AI-first notebook” launched by Google nearly two years ago. The idea behind NotebookLM is that you upload your own source materials within carefully confined notebooks, and you can then lean on Google’s Gemini AI to interact with that material in all sorts of illuminating ways. Since each notebook is limited only to whatever source materials you supply, the prevalence of those pesky hallucinations seems to be less of an issue. And since everything within your NotebookLM notebooks is kept completely private—not even used for any manner of AI model training, according to Google—you can connect it to all sorts of subjects and use it to gain a level of deep insight that was never before so easily accessible. But again, there’s the black box challenge. When you first pull up NotebookLM, it’s tough to know where to begin and how to interact with the thing in practical, approachable ways. Even as someone who writes about technology for a living and has spent more time than most mortals thinking about this service, I realized I hadn’t entirely figured out how to use it in a way that would genuinely be helpful in my day-to-day life. So I challenged myself to dig deep, get beyond all the conceptual excitement, and come up with a series of real-world use cases for NotebookLM that any regular human could both appreciate and emulate. I’ve got 15 super-specific scenarios, all tried and tested, in which the artificial intelligence answer machine could be useful for you. Follow this road map and see which path holds the most promise from your perspective. 1. Your on-demand product answer machine Up first is a possibility that’s supremely simple yet packed with productivity potential: Create a new NotebookLM notebook called “Product Manuals.” Then, every time you purchase a new appliance or device of some sort, search the web for a PDF version of its manual and add it into the notebook. If you really want to get wild, include an image of any warranty cards, too. Then, anytime you need to know anything about those products—how some part of them works, how to fix something that’s gone awry, or if and how you’re eligible for a warranty-related repair—just fire up that same NotebookLM notebook and ask, ask, ask away. 2. Your instant car support system Next, try using NotebookLM to help wrangle the most expensive gadget you own. Do a similar web search for your current vehicle’s owner manual, then drop it into its own NotebookLM notebook with the vehicle’s name as the title. Repeat for any additional vehicles you own and any new ones you purchase down the road. After recently trading in our old minivan for a hybrid Honda CR-V, my wife and I wasted far too much time flipping through the vehicle’s paper manual to try to figure out what some random button on the dashboard did. Later, after downloading a PDF of the manual from Honda’s website and then uploading it into NotebookLM, it took me all of 10 seconds to reach the same answer—simply by asking. Lesson learned. 3. An interactive car maintenance journal While we’re thinking about cars, every time you go to the mechanic, snap a photo of the service receipt and upload it into a NotebookLM notebook created specifically for that one vehicle. You can make it even more useful by uploading the same owner’s manual you found a moment ago into that notebook, too. Doing so will give you two very practical benefits: First, anytime a question comes up about what work you’ve had done on the vehicle or when a certain repair took place, you can just pull up that notebook and ask. Second, with the manual and its instructions there alongside all of your history, you can bring the two sources of info together to ask NotebookLM targeted questions that take the manufacturer’s guidance and your past services into consideration—like, for instance, when you should rotate your tires next or what other possibilities you should be thinking about at your next oil change appointment. And on a related note . . . 4. An interactive home maintenance journal Start a NotebookLM notebook for your house, then upload every invoice and estimate you get for a home repair as well as every receipt from a new appliance purchase. Whenever you next need to know when, exactly, your roof was replaced or in what year you got your current furnace—or even what brand and model it is—you’ll have a single simple place to ask and get answers. And that’s a heck of a lot easier than having an overflowing folder of assorted old papers to sift through in every such scenario. 5. Your personal company wiki Does the company you run, or maybe just work for, have more handbook-type info than any reasonably sane human could possibly ingest and remember? If so, use a dedicated NotebookLM notebook to store all of it—guides, documents, operating procedures, even lists of contacts for different departments and purposes. From that moment forward, when a question comes up about how something is supposed to work or whom you’re supposed to contact for some particular purpose, your answer will never be more than a single quick question away. 6. Your instruction-expert wizard Why limit yourself to work, maintenance, and appliances? With anything that has an instruction manual involved, dump a digital version of the document into its own NotebookLM notebook—even for board games. The next time any kind of question comes up related to those instructions, you’ve got a fast and effective way to get answers. 7. A contract deposit box Whether you’re a freelancer juggling new contracts every month, an employee signing a new agreement each year, or an employer asking dozens of workers to sign your ever-evolving documents, creating a centralized repository for all your contracts can be a real time-saver in the future. Need to remember when you last signed something with a specific person or provider? Not sure what the terms of some agreement required—or when a particular document expires? Whatever the case may be, once the info’s all in NotebookLM, you’ve always got an easy place to ask—and let the system find the answer for you. 8. Your meeting memory Provided you’re using something to record important meetings—be it a general-purpose AI-powered note-taker, a video-call-specific summarizer, or an app designed to take notes during regular audio calls—that history will be much more useful if you bring it over to a NotebookLM notebook. With such a system in place, you can simply go to NotebookLM and ask targeted questions about any of your past meetings instead of having to dig through the transcripts individually. 9. An interview inquiry station While we’re thinking about transcripts, if you conduct any kind of interviews—with job candidates, as a journalist, or for any other purpose—take each transcript and create a NotebookLM specifically for it. (Or, if you have a group of related interviews, put them all in one notebook.) Upload either the audio or the text, depending on what’s available, and then take the opportunity to ask NotebookLM questions about your conversation—be they specific (like what the person said about some particular topic) or broad (like asking NotebookLM what interesting quotes came up during the interview that you might have missed). You’ll obviously still want to refer to the full transcript at times—and to double-check the accuracy of any quote you’re actually citing anywhere—but it can be a helpful way to find something fast when you can’t remember the exact words involved or to stumble onto something you might have otherwise glossed over. 10. An intelligent feedback interpreter If your business relies on any manner of feedback to guide its operations, do yourself a favor and create a NotebookLM notebook where you can upload those results—as spreadsheets or in whatever form they take. From reviews to survey responses, you’ll then be able to ask NotebookLM to help summarize the key themes and trends, pick out recurring positive or critical responses, and even find particularly memorable quotes for potential testimonial use. 11. Your performance review reviewer For anyone managing employee performance, NotebookLM can be a major asset. Create an individual notebook for each employee and place all their performance reviews there—then, when the time comes for the next assessment, you’ll have an easy way to revisit past highlights to identify trends and provide context for comparison. 12. A financial reality checker Provided you’re comfortable with the notion, NotebookLM can turn up some really interesting insights by analyzing things like your tax returns, bank statements, and credit card statements over the years. (For what it’s worth, Google is explicit about the fact that it doesn’t in any way access, share, or use any data uploaded into NotebookLM—even for AI model training.) With that type of info in its own dedicated notebook, you can ask NotebookLM to give you an overview of your spending habits, to identify areas where you could cut back or potentially be eligible for additional tax benefits, and to surface other such pointers that you can then investigate more thoroughly on your own or with an accounting professional. 13. An audio-video reading resource Ever find yourself running into interesting-looking videos or podcasts and just not having the time or inclination to sit through them in their entirety? Make yourself a NotebookLM notebook called “Audio-Video,” then drop a link to any YouTube video or audio clip you encounter into that area. You can then ask NotebookLM for the high points—or for any specific info you’re looking to find—from any of the clips individually or even collectively. 14. An elevated reading list NotebookLM can be a fantastic way to collect links you want to read for later revisiting. With a notebook called “Reading List,” you can see the entire text of any article whose URL you add in, right then and there and in a stripped-down and simplified format—and you can ask NotebookLM for information about, or even summaries of, any or all of your saved links, too: What was that article I saved from New York a while back? Give me the most important takeaways from that Fast Company piece I saved on privacy the other day. I’m never going to catch up with everything I saved this week. Show me a summary of all the articles I added over the past seven days. You get the idea. And finally . . . 15. Your calendar companion Get a whole new level of insight into how you’re spending your time and what’s actually gone down on your calendar by exporting your complete calendar history, and then importing it into NotebookLM—where you can create a custom notebook to interact with it. In Google Calendar, this is as easy as clicking the gear-shaped icon in the desktop website’s upper-right corner, selecting “Settings,” then clicking “Import & export” in the left-of-screen side menu and clicking the “Export” option. You’ll then need to take the resulting .ics file and convert it into plain text—which you can do in a matter of seconds with a free conversion website like this one. Finally, with the resulting .txt file in a NotebookLM note, try asking questions about anything from how many meetings you attended over a given time period to how many hours you spent at the doctor’s office last year. You can also ask for specific info such as how often, on average, you get haircuts or how long it’s been since you last had a job interview. ~ google-notebooklm-calendar.jpg You might be surprised at the types of insights you uncover with your calendar data in NotebookLM’s metaphorical hands. ~ The possibilities are practically endless—and all you’ve gotta do is ask. BY FAST COMPANY

Why Google Gemini Is Emerging as a Hot New AI Tool for Startups

For years, OpenAI held the default position in most startups’ tech stacks. It was the tool founders reached for when they needed a language model, a voice engine, or a general-purpose AI backbone. But for some startups, Google’s Gemini AI has emerged as a newly preferred productivity tool, and their reasons for adopting the tech go well beyond the technology itself. Google is in the midst of an aggressive push to convince startups that its AI solutions are superior. Leading that charge is Darren Mowry, head of Google Cloud’s global startup team. Mowry confirms that yes, Gemini use is rising among startups, and it’s resulting in new business for Google Cloud, which is the only way for businesses to use the Gemini API. Instead of just automatically selecting Amazon Web Services as their cloud provider, Mowry says, new startups are now choosing Google Cloud in part so they can get access to Gemini. Google has always been central to the AI business; in 2017, the company released a seminal AI research paper called “Attention is All You Need,” which introduced the “transformer” architecture that makes modern AI models possible. But up until last year, the company lagged behind its competitors when it came to business adoption. When Gemini was launched in 2023, it was originally named Bard, and it quickly developed a reputation for hallucinating facts. Remember when it recommended that people put glue on pizza? That changed in April 2025, when Google released Gemini 2.5 Flash, a model that handily beat OpenAI across a number of benchmarks, and according to Mowry, ignited a wave of interest in Google’s AI offerings that has only grown with the release of subsequent models. One of the factors that differentiates Google from its competitors is that it offers a fully vertically-integrated solution, in which the company can handle each part of the tech stack. Not only can startups choose from a wide selection of Google-made and external models on Google Cloud, Mowry says, but the company can also provide those startups with technical assistance to help make sure they’re getting the most out of both the models and the Google-made chips they run on. According to Mowry, this vertical integration “shrinks down the time” it takes for founders to build.” Some founders are finding that Gemini is a useful way for non-technical employees to enjoy the benefits that software developers have gotten from agentic coding tools like Claude Code. Aakash Shah, founder and CEO of allergy care startup Wyndly, says that while his engineers have gravitated toward Anthropic, his operations team wanted to use Gemini in the applications they’re already comfortable with, like Google Docs and Gmail. A common use case? Asking Gemini “who did I email on such-and-such day?” Shah says that everyone at his company now has Gemini, enabling them to chat with Gemini across the entire Google Workspace suite, including Gmail, Docs, Sheets, Meet, and Notebook LM, Google’s app that turns documents into audio podcasts. “I’m trying to get everyone to be AI-first,” Shah says, “and part of that is helping them use it where they already are instead of forcing it on them.” Sheltered International, a customs broker and freight forwarder that primarily deals with international imports, is currently using Gemini to help speed up the process of filling out customs paperwork. Founder Andrew Ciccarone says that when a shipment comes in, his company is responsible for verifying its commercial invoice data and ensuring that it’s marked with the correct Harmonized Tariff (HTS) code. According to Ciccarone, Sheltered International has started using a fine-tuned Gemini model to extract relevant information from the commercial invoice data (which often comes in the form of a PDF), validate it, and reformat it into an Excel spreadsheet. The Gemini models are considered to have state-of-the-art computer vision, enabling them to examine images and documents in incredibly granular detail. “What the AI can do is just give us a huge leap forward before the customs broker comes in to ensure everything is classified correctly,” says Ciccarone, adding that for a small operation handling the complexity of international trade documentation, Gemini has streamlined a process that used to be painfully manual. When this process was fully done by humans, it required hours of manual scanning through lengthy, unformatted documents. Still, Ciccarone admits that the company isn’t saving that much time yet, because employees still need to verify that the AI’s output is accurate. But as the fine-tuned Gemini model improves, he expects to see a significant increase in productivity. Companies are also integrating Gemini’s machine vision abilities into their actual products. Take Validity, a startup that sells itself as an all-in-one solution for the entire email marketing process. Validity chief technology officer Matt Gore says that the company’s newest product, a platform called Validity Engage, has been largely built around Gemini’s capabilities. Engage gives marketers access to four purpose-built AI agents that can analyze, optimize, and reformat emails according to internal campaign style guides. Using Gemini 3 Pro, Validity can now detect and fix granular visual details in emails (like whether a certain font matches a brand’s approved style guide) that no model could reliably catch a year ago. For instance, Gore says, emails can often appear illegible to computers and phones that are set to dark mode; with Validity Engage, marketers can guarantee that their email will be visible to everyone who received it. Geo says that Validity decided to “hitch our wagon to Gemini” following extensive testing. When developing the new feature, Gore used an orchestration tool called Mastra.ai to compare how various models approached roughly 150 common email issues, taking note of each model’s cost and speed. In the testing, Gore says, Gemini 3 Pro stood out as being “just leaps and bounds ahead of others in terms of computer vision.” He says that the Gemini 3 models are particularly good at identifying the “bounding box” of the email—basically the frame containing the email’s content. Beyond computer vision and text, Gemini’s speech capabilities have been a major selling point for founders. David Yang, founder of solopreneur-focused AI receptionist company Newo, is using Gemini to provide his virtual receptionists with voices. Yang founded Newo to help solo founders capture more inbound leads by giving everyone access to an always-on receptionist who can answer a phone call at any hour of the day. But for Newo to work, Yang needed voice models that have extremely low latency and high emotional intelligence. Originally, the company’s AI receptionists were powered by OpenAI’s text-to-speech and speech-to-text models, but the lag between asking a question and hearing an answer was too long. Now, Yang says that Newo uses Gemini 2.5 Flash Native Audio, a recently-released model that can understand and generate audio in real time. Not only is the new model incredibly fast, Yang says, it can also understand emotional intent, an important data point that’s usually lost with more traditional speech-to-text transcription models. As part of his push to bring startups into the Google ecosystem, Mowry says his team is currently hiring engineers and former founders to staff up a “founder advocacy” group. These employees “sole purpose in life” will be “to wake up and meet founders that have really big problems,” he says, and “help them move from ideation into actually getting things built.” The goal is to “catch these cohorts of startups early, give them a little bit of credit assistance, engineering assistance, and help them get off the ground.” This soup-to-nuts approach is helping Google win startup business, and positioning the company as the new default AI partner for the next generation of businesses. BY BEN SHERRY @BENLUCASSHERRY

3 Ways Digital Tools and AI Help Simplify Tax Season

Tax season used to be my least favorite part of being a creative small business owner. It always felt overwhelming. ​As the founder of Mochi Kids and Mochi Play Store, I juggle designing and producing children’s clothing, managing inventory and wholesale orders, and running a brick-and-mortar store. ​ When tax time hits, all the information needed to run my business has to be accurate, organized and easy to find. To make the tax process more manageable, I’ve started relying digital tools like Adobe Acrobat to stay organized and prepared. Here’s how I approach tax prep, step-by-step, to simplify everything. ​ Step 1: Digitizing paperwork before it piles up Paper forms used to be my downfall. I’d toss them into a folder and promise myself I’d deal with them later. ​By tax season, “later” meant hours of sorting and searching. ​Now, I use Adobe Scan to digitize receipts, tax forms, and donation confirmations as soon as I receive them. ​I simply snap a photo with my phone, and the app converts it into a clear, searchable PDF. I name each file and save it to a folder labeled for the current tax year. Later, I can search documents by vendor, keyword, or dollar amount. ​ Step 2: Organizing documents by category Once everything is digitized, I focus on organizing. Instead of keeping dozens of separate files, I use Acrobat’s Combine and Organize tools to merge related documents into a single file and sort them by category. ​For example, I combine PDFs for charitable contributions, income, expenses, and deductions. ​Acrobat makes it easy to reorder pages, delete duplicates, and add bookmarks so I can quickly find what I need. ​This is especially helpful when preparing and double-checking documents for my accountant before filing. Step 3: Protecting and signing your tax documents with AI Tax documents contain some very sensitive information, so security is non-negotiable. ​Before sharing files, I password protect PDFs and limit access to only the people who need them. ​I can use Protect & Sign with AI Assistant to password protect sensitive information or sign the documents for me. Taking a few extra seconds to secure files gives me peace of mind. A calmer way to approach tax season Tax season may never be exciting, but it doesn’t have to be chaotic. ​By scanning documents early, organizing them thoughtfully, protecting sensitive information, and handling signatures digitally, I’ve made the process far more manageable. ​This allows me to focus on the fun parts of running a creative small business. Why I trust Adobe Acrobat for my tax prep Adobe Acrobat has been a gamechanger for me. It helps me stay organized, save time, and feel confident that my sensitive information is secure. ​Whether you’re an individual filer, freelancer, or small business owner, Acrobat has the tools to make tax season less stressful. From digitizing paper documents to organizing files and securing sensitive information, it’s the ultimate tax prep partner. Here’s to a stress-free tax season! ​ BY AMANDA STEWART FOR ADOBE

This Single ChatGPT Prompt Can Do Hours of Market Research in Minutes—Here’s How

Market research can be a slow, fragmented, and difficult process, often involving tedious internet searches, questionable data sources, and time-consuming manual synthesis. This makes it a great candidate for some assistance from AI. What’s more, an update to a popular feature on ChatGPT has made it even better at doing this kind of work. Imagine that you have a potential business idea but still need to validate how viable it actually is, identify primary competitors in your market, and develop an ideal customer persona. Instead of spending hours collating data, explains Dan McCarthy, an associate professor of marketing at the University of Maryland, you can use Deep Research, a ChatGPT feature that directs an AI agent to develop a comprehensive, well-cited report on any topic. Last week, OpenAI upgraded Deep Research with some new abilities. The feature now runs on GPT-5.2, one of the company’s most recent models (previously it ran on a much older o3 model), and can now prioritize specific websites in its search process. Deep Research is available for all paid ChatGPT users. Here’s how to use it to get some thorough market research done quickly. Step 1: Get your prompt right To test out how this feature could help with market research, I pretended that I wanted to start a digital transformation firm based in Denver with a focus on upgrading bars with mobile, bar-to-table ordering capabilities. All I needed to do in order to get started was click the plus button next to the text box, select More, then Deep Research, and enter a prompt. This prompt will determine the information that ChatGPT prioritizes in its search, so it helps to be verbose. If you need help developing a lengthy prompt, try using ChatGPT to help write it. McCarthy, who uses AI tools extensively, says that an easy way to develop a comprehensive prompt is to activate the chatbot’s voice mode and simply have a conversation with it. Once you’ve explained what you want, McCarthy says, you can ask ChatGPT, “Given all this that I’m telling you, what do you think would be the best thing that I should even be asking you?” That should help clear up any blind spots you might’ve missed. According to McCarthy, this method should produce a solid prompt that you can give to the Deep Research agent. When I asked ChatGPT to help expand my prompt, the platform generated a 673-word result. This prompt (which you can view here) defined the agent as a market research analyst and gave it objectives to determine the business idea’s viability, map out the competition, and define my ideal customer’s persona. Additionally, it provided details on the scope of the research, and information for how the agent should format its report. I also used ChatGPT to develop a list of specific websites for the Deep Research agent to prioritize in its search. Step 2: Start the research I entered my ChatGPT-created prompt, selected the Deep Research feature, and pressed return. Before getting to work, the agent broke down its objectives into the following bullet points: Collect primary vendor docs and pricing pages starting with user-preferred sites. Survey industry, local Denver sources, and hospitality reports for market context. Compile POS integration lists, local competitors, and implementation partners in Denver. Analyze demand, model ROI scenarios, and estimate Denver bar counts and adoption rates. Draft recommendations, ICP personas, GTM plan, and cite sources with confidence ratings. Over the next 21 minutes, the agent searched through hundreds of web pages. It found liquor license databases, census information, and data regarding competitors in Denver’s hospitality-focused digital transformation market. It compiled all this information into a multi-section report. Step 3: Read the report That report (which you can view here) ended up being roughly 4,000 words. It included an overview of the market, identified customer pain points, and listed out my potential competitors. The report also included recommendations for how to position my business, strategies to break into the Denver hospitality scene, and even identified a small business that would likely be my direct competitor: a Denver-based POS integrator called Megabite. ChatGPT found that while my business idea had potential, it wouldn’t fully meet the needs of Denver-based bar owners, who have reported that bar-to-table ordering can actually lead to fewer sales and tips. Instead, the report suggested, I should consider a system that can sit on top of popular POS in which diners don’t need to pay for every new drink they order, and can instead open a digital tab. What the expert thinks of the result McCarthy told me he was impressed by the report that Deep Research produced. In particular, he was pleasantly surprised by the agent’s cleverness in using liquor licenses to get a sense of the market size, and its thoughtfulness in calling out disruption to bar culture as a potential blocker to the business. But the report wasn’t perfect. McCarthy said much of what was included was unnecessary or needlessly complex. An easy prompt to fix this? “Just tell it, ‘Explain it to me like I’m an idiot.’” McCarthy adds, “I do that all the time.” He says that a solid market research report should also answer questions regarding the scope of adoption and how often repeat purchasing is expected. McCarthy also says that users should direct the Deep Research agent to be very upfront about the data it attempted to get but couldn’t. Many websites block AI agents from engaging with their content to prevent data scraping, which can hinder the research process. By telling your agent to list out the sites that it couldn’t access, you can manually obtain that data and add it to the analysis. Our bar-to-table digital transformation firm will have to remain a pipe dream for now, but it’s clear that AI has made the process of taking an idea from zero to one easier and faster than ever. If you have an idea for a new business or are planning on an expansion or pivot in your current business, consider giving Deep Research a spin. It might unearth something that makes you think in a different way. BY BEN SHERRY @BENLUCASSHERRY