When I first started using NotebookLM, I treated it like a research assistant. I uploaded notes, asked for summaries, and used it to organise information. It did that job well, so I never thought much beyond it. Then, almost by accident, I began asking it to handle entirely different tasks. One small experiment turned into another, and soon I realised I had been overlooking one of its biggest strengths. NotebookLM is not just about research. It works surprisingly well with everyday tasks that save time and reduce effort. If you already use it or are thinking about trying it, these five uses completely changed how I fit NotebookLM into my daily routine.
Reading summaries is useful, but I noticed that while I understood it while reading, I forgot most of it a day later. Hence, I decided to find a way to make studying more active. Something that can help me to stay focused while revising and make it less repetitive.
So, instead of treating NotebookLM like another tool for generating summaries, I asked it to create riddles from my notes, and the result surprised me. Instead of giving me the answers, the tool gave me clues that made me think. I had to remember what I learned, connect ideas, and solve each puzzle before going to the next one.
It quickly became one of my favourite ways to revise. If I have a topic with several pages of notes, I ask NotebookLM to generate ten riddles that range from easy to difficult. It no longer seems like studying but just playing a game of quizzes with me. This technique has worked wonders for me whenever I have applied it before interviews or presentations.
Instead of rereading the same pages again and again, I had to challenge myself to solve the clues from memory. This turns revision into an active exercise rather than a passive one. Because I have to recall the information instead of simply recognising it on the page, I remember it for much longer.
Another feature that made the app even better is its ability to understand images. Occasionally I take photos of handwritten notes or screenshots from presentations. I simply upload them, and NotebookLM reads the text almost like an OCR tool. Once those images become part of my notebook, it can create riddles from them just like any other document.
Also read: OpenAI finds AI agent leaving escape notes for its future versions: Report
Some of my best ideas appear when I am nowhere near my computer, and I think most of you will relate that the ideas flow so fast that we can’t type at that pace, so instead of opening a notes app and typing everything, I record a quick voice memo.
No, this isn’t the problem but rather the easy part, as the problem comes later. Listening to a ten-minute recording of my own thoughts is rarely enjoyable. I jump from one idea to another, repeat myself, and sometimes forget what I wanted to say halfway through.
NotebookLM fixes that problem for me, as I just upload the recording as a source and ask it to create a transcript. Within minutes, I receive clean text that is much easier to read. Moreover, it also gives me a short summary and points out the main ideas that appeared throughout the recording.
I’ve also used this with longer audio files, such as when I needed to find a specific point in an hours-long meeting or a particular quote from an interview. It makes reviewing recordings much easier because I no longer have to scrub through hours of audio. Instead, I can simply search the transcript or ask NotebookLM specific questions about the discussion.
One unexpected trick I discovered is using it with NotebookLM’s own Audio Overviews. After downloading an overview, I upload it again as a source. NotebookLM creates a transcript, making it easier to search for important points without replaying the entire audio.
Being in the tech media industry, I have to keep an eye on leaks and rumours. But not every leak is in a language I can read or understand. This is where NotebookLM comes in handy, as all you need to do is upload all the documents in the tool and just ask it to explain everything in plain English.
Rather than translating each sentence individually, the tool analyses the document and provides a clear and detailed explanation for it in a single go. Not only that but it also responds to any queries that I may throw at it.
Deciding what to cook is sometimes harder than cooking itself. Like many people, I have collected recipes over the years. Some are stored in my notes, while others come from family recipes or cooking experiments that worked well enough to save.
Instead of scrolling through dozens of files, I uploaded my recipe collection to NotebookLM. Now, when I cannot decide what to make, I simply ask for ideas.
The responses feel much more personal because NotebookLM only works with recipes I already like. It does not send me searching through random cooking websites. Everything comes from my own collection. One feature I use almost every week is asking it to work backward.
I tell it exactly what ingredients I have left in the kitchen. Maybe there is chicken, spinach, rice, eggs, and a few vegetables. NotebookLM searches through my uploaded recipes and suggests meals that match those ingredients. If something important is missing, it tells me exactly what I need.
It has helped reduce food waste because I actually use ingredients before they expire. It has also saved me from those evenings when I spend twenty minutes searching for a recipe only to end up ordering food instead. While it cannot cook dinner for me, it definitely makes the decision much easier.
I have never enjoyed reading instruction manuals, and even most of us ignore them until something goes wrong. Then we spend several minutes flipping through page after page looking for one small answer. That changed when I started uploading manuals to NotebookLM.
Whenever I buy a new device, I save the digital manual inside its own notebook. I have done this for household appliances, electronics, my car, and even my air fryer. Now, instead of searching manually, I simply ask questions.
If I want to know the best temperature for frozen fries in my air fryer, NotebookLM gives me the answer in seconds. If I need to understand a warning light in my car manual, I can ask it instead of searching through hundreds of pages. It also shows where it found the answer, so I can quickly check the information myself. This works because NotebookLM searches only within the uploaded manual. It does not guess or pull information from unrelated sources.
Of course, it cannot answer questions that the manual never explains. If the original document is vague, the response will also be limited. But when the manual is detailed, finding information becomes much faster than reading the entire document yourself.