I ignored this NotebookLM feature for months — now I can’t research without it

Folders are so burnt into our habits that it’s tough to think without them. Well, NotebookLM’s “notebooks” are one way to look at folders. But upload 50 sources into them, and it turns into a highway pileup. Once my notebooks started growing past 20 or 30 sources, the Sources panel became a nightmare to navigate. Labels are fixing that problem. They changed how I organize my research in NotebookLM from the start.

When you have more than five sources, an Auto-label button appears in the Sources panel of NotebookLM. One click and NotebookLM analyzes all your sources and creates thematic categories. This tiny automatic feature can be used in several creative ways.

Auto-labeling cuts the chaos instantly

One click can organize a messy notebook

Once you have five or more sources in a notebook, an Auto-label button appears in the sources panel. Click it and NotebookLM reads the content of every source and sorts them into thematic clusters. The AI actually reads and automatically clusters your sources into high-level categories. You don’t have to rename your sources or worry about upload order.

I expected generic labels that could mean anything on everything. But the clusters it produces are quite accurate. A notebook I’d built for a project on lifelong learning gave me an instant grouping of what I’d collected. Sometimes, a single feature can change the way you do your research in NotebookLM.

The labels don’t need to be permanent. If you want to see your traditional layout, choose Return to list view. NotebookLM allows you to go back and forth. To build your own label names, you can create a new label, rename it, and manually check it off for relevant sources.

Labels reveal gaps before you start

See the blind spots in your sources at a glance

Once your sources are labeled, the panel becomes a visual audit of your research. A thin cluster of a single source under “Psychology of Learning” tells you something before you write a single word. Again, a label with ten sources might mean you’re over-indexed on one angle and under-covered on another. What we can aim for is balance.

Earlier, with a long scroll of sources, it was impossible to get this umbrella view. I just used to check and uncheck the documents and check the summaries as my first project onboarding step. Now, I can evaluate research quality before starting with the prompts.

So, scan the label clusters. If one looks thin, add more sources. When you add new documents, they won’t scramble your existing layout. Instead, they appear in an alphabetical list below your labeled categories as unlabeled sources. To sort them, click the auto-label button and select Reorganize unlabeled sources.

You can filter sources mid-conversation

Focus NotebookLM’s tools on exactly what you need

Labels are little sandboxes. You can toggle entire label groups on and off while you’re chatting with NotebookLM. Select one or two and switch off everything else. The AI will reply with grounded answers based on whatever sources are active. For instance, building a section based on case studies? Activate only that cluster. It’s like an additional layer of a private knowledge base.

I used to think that this was unnecessary as NotebookLM’s response is based only on our uploaded materials. But focused sources produce better answers. When I use even well-designed NotebookLM prompts across 30 sources, the response pulls in stuff I don’t need from other topics.

Narrowed to one labeled cluster, the answers are sharper, less contaminated, and easier to fact-check. Toggling takes five seconds. I am not sure, but it probably speeds up chat response times since the context window is narrowed. You can use the chat tool to find out what your research is missing.

Based strictly on the sources in this specific cluster, what are the logical gaps, missing data points, or unaddressed perspectives?

One source can belong to multiple labels

Overlapping topics don’t need duplicate files

NotebookLM lets a single source carry more than one label. A research paper on “The Synergy of Spaced Repetition and Retrieval Practice” might land under both “Spaced Repetition” and “Learning Strategies.” A market report could sit in “Data Sources” and “Competitive Analysis” at the same time. The system tags it wherever it fits.

Here labels don’t work like folders; it’s more like a tagging system than a filing cabinet. This multi-label support helps a lot with research. Your sources show up wherever they’re relevant without any duplication or manual copying.

You can now pit clusters against each other to find research gold. For example, select two opposing or adjacent categories and prompt the chat (see screenshot in the gallery above):

Analyze the contradictions, friction points, or core disagreements between the sources in Label A and the sources in Label B.

Use clusters for precise Studio outputs

Chunk your learning and retrieval with Labels

Instead of generating a generic Audio Overview, Slide Deck, or Flashcard set for your entire notebook, you can select a single cluster and generate a studio output.

This produces an incredibly hyper-focused podcast or deck devoted entirely to that sub-topic without getting diluted by the rest of the notebook. For instance, I have always found it tough to handle a mind map from too many sources on the limited screen of a laptop.

I have also found this especially helpful for enhancing the quality of NotebookLM’s Audio Overview. A large NotebookLM notebook can lead to a rambling podcast from the AI hosts. Keeping it specific is not only a timesaver, but also makes it easier to slice and dice the podcast with your own follow-up questions.

OS

Android, iOS, Web-based app

Developer

Google

Pricing model

Free

NotebookLM is Google’s AI-powered research notebook that reads what you upload and helps you transform it into structured summaries, explanations, and visuals.

Try Labels on your messiest notebook

Labels won’t reorganize your thinking. But they will show you exactly what you’ve been working with all along. And that’s often the clearest starting point for research that you want to take somewhere. Use it to spot folders that are way too empty, and point out topics you totally forgot about.