NotebookLM is everybody’s favorite AI research assistant, but I also use it in my day-to-day productivity. I’ve started offloading a bunch of repetitive mental chores that used to require several different apps, tens of open browser tabs, and additional focus. But why put so much effort into small tasks when an AI tool can speed them up tenfold? In a way, NotebookLM has become my second brain.

Much of my “busy” work wasn’t really work at all, but rather passive tasks and background admin that gobble up time. It’s the mundane stuff like reading through old documents to find a specific piece of information or skipping through a YouTube video to quickly get to a key point. NotebookLM now automates much of that for me. These are the boring, everyday tasks I’ve started offloading on the AI…

Learning how to use new tools

No more hour-long tutorials

I’m not talking about user-friendly apps like Google Keep, but the more complicated ones I use in my creative workflows, like Krita or Penpot. Before NotebookLM, learning a new app or software used to mean spending a lot of time figuring it out myself, reading all the official documentation, or sitting through YouTube tutorials. Now I just feed the docs and tutorials into NotebookLM and prompt it to help me get started.

It’s actually quite a fun way to go about it; it makes the learning process much less boring because you can completely customize your learning experience with prompts. For example, I can tell NotebookLM that I’m a complete beginner, then prompt it to give me the best starting point for the specific task I want to use an app for. This way, I don’t have to sort through a million sources manually to find the exact set of features I need for my goal.

The downside to this method is that you might miss some fundamental steps that are necessary for building muscle memory with software. But that’s why crafting the right prompt is so important – you have to tell NotebookLM exactly what level you’re at and how much of the basics you don’t understand yet, before jumping into your goal task.

Fetching old information

It’s like having an AI vault

Fetching information from older documents is a two-part problem for me: Not only can it be difficult to remember which documents to look at, but most of the time I don’t even remember which folder or app it lives in. This is why I started offloading all my important documents into a personal notebook. This includes journal entries, personal achievements and progress, finances, and so on. It’s the same concept as having an “everything” notebook, or feeding your Obsidian vault into a notebook.

This is really where NotebookLM shines – it’s one of the most powerful retrieval tools out there. Not only can I instruct it to fetch what I need, but I can have it give me additional context, too. For example, I can retrieve my expenses for a specific month from years ago, then also compare it to my expense patterns for the current year. And if I’m not even sure what I’m looking for, NotebookLM usually does a good job at finding what it thinks I’m looking for with a very general prompt.

Quick reminders

So I don’t have to repeat my learning or writing

In the same vein as above, NotebookLM is great at reminding you of things you’ve already learned or written. It doesn’t have traditional reminder features like notifications or task lists, but it remembers context better than I do. This is especially helpful with some of my design studies and novel writing. When I start researching a topic again, NotebookLM can pull insights or notes I made months ago and show me I’ve already done half the work, or give me a quick reminder of basic information. This prevents me from wasting time revisiting my research and notes – as long as I remain consistent with updating my notebook.

Writing comparisons

Settling on a writing tone without overthinking it

My writing style and tone need to adapt to the type of content I’m writing. Writing an article like this is different from a novel, social media post, or a design case study. NotebookLM helps me check whether my tone, pacing, and structure make sense for each one. I’ll periodically add some of my own drafts to a notebook, complete with the details of what they’re for. And then when I’m uncertain about a draft I’m currently writing, I’ll add it as a source and ask NotebookLM to compare it to my other writing formats. This helps me pinpoint where I might have missed the mark.

Translating jargon

Making technical language readable

Every field has its own language, and understanding them, let alone switching between them, can be exhausting. Half the time, I spend longer decoding what people meant than actually using the information. For me, this is the case with UX design, learning about AI tools, and also some psychology subjects. These are the topics I engage with every day, and NotebookLM is excellent at translating them to me in plain language.

For example, just recently I learned that “context window” refers to the amount of text an AI can remember or reference at once; and “affordance” is the visual cues that tell a user of a digital product how something should be used, like a button that looks pressable. I could just Google these things, but that usually leads me down rabbit holes or still not getting a full grasp. And I can’t spend my entire day searching for definitions. NotebookLM helps me understand much faster than a search engine ever could, because it keeps everything in context and personalized to my learning style.

Letting NotebookLM handle the boring tasks

I primarily use NotebookLM for its intended purpose – research and learning. But it’s also become a quiet assistant that handles the small, boring stuff in the background. Offloading these tasks not only saves time, but also cognitive clutter. This allows me to focus more on the stuff I actually care about rather than the repetitive busywork.