Folders have become so ingrained in our habits that it's hard to imagine doing anything without them. Actually, NotebookLM's "notebook" is a way to view folders. But if you upload 50 sources to it, it becomes a mess. When notebooks start exceeding 20 or 30 sources, the Sources panel becomes difficult to navigate. Labels will help solve that problem. They will change the way you organize your research in NotebookLM right from the start.
When you have more than 5 sources, the Auto-label button will appear in NotebookLM's Sources panel. With just one click, NotebookLM will analyze all your sources and generate themed categories. This little automatic feature can be used in many creative ways.
The auto-label feature eliminates clutter instantly.
With just one click, you can reorganize a messy notebook.
When you have five or more sources in a notebook, an Auto-label button will appear in the Sources panel. Click on it, and NotebookLM will read the content of each source and organize them into topic clusters. The AI truly reads and automatically groups your sources into high-level categories. You don't need to rename sources or worry about upload order.
The clusters it generates are quite accurate. Sometimes, a single feature can change the way you conduct research in NotebookLM.
Labels don't need to be fixed. If you want to see your traditional layout, select Return to list view . NotebookLM lets you switch back and forth. To create your own label names, you can create a new label, rename it, and manually mark up the relevant sources.
Labels help you identify gaps before you begin.
Quickly identify blind spots in your source material.
Once the sources are labeled, this table becomes a visual checklist for your research. A cluster of few sources under "Psychology of Learning" tells you something before you write any words. Conversely, a label with 10 sources means you're focusing too much on one aspect and not adequately covering another. The goal is balance.
Previously, with a long list of sources, getting such an overview was impossible. You would typically just select and deselect documents and check the abstracts as the first step when starting a project. Now, you can assess the quality of research before you begin with prompts.
Therefore, consider the label clusters. If a cluster appears to have few sources, add more. When you add new documents, they won't disrupt the existing layout. Instead, they appear in an alphabetical list below your already labeled categories as unlabeled sources. To organize them, click the Auto-label button and select Reorganize unlabeled sources .
Note : A full rearrangement will erase all custom edits and rebuild the groups from scratch.
You can filter sources while chatting.
Focus NotebookLM's tools on exactly what you need.
Labels are like little sandboxes. You can turn entire groups of labels on and off while chatting with NotebookLM. Select one or two labels and turn off all the others. The AI will respond with well-founded answers based on whatever source is active. For example, build a section based on case studies? Just enable that group. It's like an extra layer of private knowledge base.
Many people used to think this was unnecessary because NotebookLM's responses were based solely on the documents we uploaded. But focused sources produce better answers. When you use even NotebookLM's well-designed prompts across 30 sources, the answers still include things you don't need from other topics.
Narrowing down responses to a labeled group makes them sharper, less cluttered, and easier to verify. The conversion takes 5 seconds. It speeds up chat responses because the contextual window is narrowed. You can use the chat tool to identify shortcomings in your research.
Based on the sources within this specific group, what are the logical gaps, missing data points, or aspects that have not been addressed?
A source can belong to multiple labels.
NotebookLM allows a single source to carry more than one label. A research paper on "The Synergy of Spaced Repetition and Retrieval Practice" can be in both "Spaced Repetition" and "Learning Strategies." A market report can be in both "Data Sources" and "Competitive Analysis" simultaneously. The system will tag it wherever appropriate.
Here, labels don't function like folders; it's more like a tagging system than a filing cabinet. This multi-label support feature is incredibly helpful for research. Your sources appear wherever they're relevant without needing to be manually copied.
Now you can compare clusters to find valuable research sources. For example, select two opposing or adjacent categories and suggest a conversation (see screenshot in the gallery above):
Analyze the core contradictions, points of conflict, or disagreements between the sources in Label A and the sources in Label B.
Use label clusters to produce accurate Studio results.
Break down the learning and information retrieval process using Labels.
Instead of creating a generic Audio Overview, Slide Deck, or Flashcard set for your entire notebook, you can select a single set of labels and generate Studio results.
This creates an extremely focused podcast or presentation, entirely dedicated to that subtopic, without being diluted by the rest of the notebook. For example, many people always find it difficult to process mind maps from too many sources on the limited screen of a laptop.
This is especially helpful for improving the quality of NotebookLM's Audio Overview. A large NotebookLM notebook can result in a rambling podcast from the AI host. Keeping it specific not only saves time but also makes it easier to analyze and add your own follow-up questions to the podcast.
Try Labels on your messiest notebook!
Labels won't reorganize your thinking. But they will show you exactly what you've been working on so far. And that's often the clearest starting point for the research you want to do. Use it to spot folders that are too empty and point out topics you've completely forgotten about.