NotebookLM has been the talk of the town amongst productivity circles for a while now. I’ve seen people rave about it, but none of the popular use cases truly clicked for me.
I have my note-taking process down to a science and don’t need NotebookLM for that. Nor am I a student who needs flashcards and help with learning.
But as a journalist, research is something I could use help with. So when NotebookLM added a new Deep Research tool earlier this month, I decided to give it another shot.
Instead of taking it straight into work mode, I decided to use it for something decidedly different — trip planning. Long story short, I get it now.
Clubbed with Deep Research, NotebookLM is elevated to a whole new level and starts feeling like a practical tool that can offload a lot of the cognitive load of online research.
NotebookLM does this by stitching together information that is already out and combining it with your own vetted source of information, presenting it in a neutral, grounded, and research-driven style.
The goal here is to collect and collate information, and the more I use it, the more ways I find to integrate it into my workflow.
NotebookLM reshaped my trip planning workflow
Using Deep Research to turn scattered links into a practical travel plan
The trip planning experience is where my recent rabbit hole into NotebookLM’s capabilities started.
I am in the midst of planning a week-long trip to Japan. Considering the language barrier, sheer breadth of places I want to visit, and the general complexity of visiting a new country, the logistics have come across as pretty overwhelming to me.
That’s over and above the fact that you need to track flight prices, understand the nitty-gritty of rail passes, pick hotels, research travel spots, cafés, restaurants, and more.
Usually, I trawl through a dozen or so tabs, screenshots, Instagram videos, and info dump all of this into Google Docs. NotebookLM has completely transformed this process for me.
Instead of letting these dozens of tabs sit around, I now add them as reference documents into NotebookLM. This could be something as basic as a list of places I want to visit, hotel shortlists, and a vast number of links that I’ve saved.
I then give NotebookLM a straightforward prompt: Help me build a daily plan based on these references and pull any missing information from the web.
As expected, the deep research tool took a few minutes to create an extensive research report based on my research topic, pulling information from hundreds of websites, much more than I’d have been able to achieve manually.
It gathered all this information from verified sources, gathered practical details, and returned a very grounded summary similar to the deep research tool built into Google Gemini itself.
This included real-world estimates of transit times, restaurants to explore, ticket prices, expected crowds, and more. It flagged travel aspects I’d missed, and travel spots that weren’t open at that time of the year.
Now, the research bit in itself was impressive. Deep Research didn’t just present an info-dump. Instead, it organized it in a meaningful way, similar to an executive summary, to give me all relevant information and sources for travel planning.
But unlike regular AI search, it didn’t just swap out everything else in lieu of its own research. You see, Deep Research in NotebookLM works in tandem with your specific notes, research, and links. This allows you to have conversations that span all these research points.
It’s like working with your own research assistant, just incredibly smart and with access to all the information in the world.
It also allows you to do things like add follow-up questions to your chat. Like, for example, suggest alternatives if a spot is too crowded, or a realistic amount of time to spend here.
While my personal research can pinpoint locations, deep research brings in real-world context through extensive research. It’s great.
How NotebookLM fits into my everyday research
Building better context by combining documents and web research
While trip planning was where I got into NotebookLM, I’ve since found many more use cases for the tool.
The realization that it works best when you feed it context, and it builds around that, has helped amplify my personal research. This could be dumping an assortment of PDFs into a single notebook and adding to it with internet research.
I use this workflow when I need personally organized base information with external information sources that get added to it, and point out areas where I need to follow up with extra context.
More importantly, unlike standard AI chats, NotebookLM sticks to the context I’ve given it and has been researched so that it avoids hallucinations or going out of context.
Moreover, NotebookLM lets me build on the research as I add additional research points.
Deep Research finally made NotebookLM click for me
While NotebookLM has always been a useful tool, I’ve struggled with adding it to my personal use case.
The addition of Deep Research changes that. It’s not a replacement for your standard search tools; Instead, it’s a full-fledged research tool that helps you connect the dots across personal research and the broader internet.
All of that combined means that NotebookLM has earned its place in my daily productivity stack.
