Quick verdict
NotebookLM offers a more useful approach than an ordinary chatbot for students, researchers, editors, and project teams that need to read and compare many documents: It bases its answers on sources selected by the user and adds inline citations to the relevant passages. This can significantly speed up finding a specific claim in a large folder of PDFs or identifying common points across several reports. However, the tool does not guarantee the accuracy of those sources on its own; if well-organized misinformation is uploaded, it may summarize it convincingly.
What can it import?
In the web version, different source types—including PDFs, web pages, YouTube videos, audio files, Google Docs, and Google Slides—can be collected in the same notebook. This variety is particularly valuable for desk research. When preparing a product review, for example, you can add the user manual, the manufacturer’s support page, an independent study, and your own interview recording to the same workspace.
The most efficient method is to create a separate notebook for each topic and organize source names clearly. Filling one space with dozens of unrelated files blurs the scope of the answers. Rather than publishing a draft directly, you should also begin with a verification question such as, “Show the sources and relevant passages supporting this conclusion.”
How useful are the answers and citations?
NotebookLM’s clear advantage is that it relies on the uploaded material instead of freely generating answers from the model’s general knowledge. You can click citations to see the context of a claim within the document. This narrows the gap between an answer and its evidence that remains in a conventional chat tool. Still, the presence of a citation does not mean the interpretation is error-free. The model may misread a table heading, merge two different date ranges, or weaken an author’s caveat while summarizing it.
A practical verification process can have three stages: First obtain a general summary, then ask separate questions about critical claims, and finally read the cited original passage yourself. Numerical values, health and legal information, direct quotations, and current prices must always be checked against the primary source.
Audio overviews, reports, and mind maps
The tool does not use sources solely in a question-and-answer format. It can also create outputs such as study guides, briefings, reports, audio overviews, and mind maps. Audio overviews are useful for listening to the main topics of long documents while walking, but they do not replace detailed reading. A fluent presentation can make an uncertain inference feel more definite than it really is.
A mind map is effective for quickly seeing the main themes in a collection of sources and asking a new question through a particular node. According to Google’s help documentation, this feature is not yet supported in the mobile app. Although the mobile version is convenient for adding a web page or PDF to a notebook through the share menu, some desktop functions—such as generating notes, reports, and data tables—may not be available on mobile. A more balanced workflow is therefore to use the browser for intensive production and the mobile app for collecting and listening to sources while on the move.
Experience using it in Turkish
NotebookLM supports more than 80 languages and can conduct question-and-answer sessions with Turkish sources. For the best results, it helps to state the output language explicitly in the prompt, define the Turkish equivalents of terms, and specify whether sources in other languages should be translated or compared in their original language. For technical concepts, retaining the original English term in parentheses makes it easier to search the sources later.
Who is it for, and who should look for another solution?
People who regularly produce briefings from numerous sources, turn course materials into question banks, or compare interview recordings with documents can save substantial time. By contrast, the system may be unnecessarily heavy for someone who only wants to keep short notes. Mobile users expecting complete feature parity will also encounter limitations.
Confidential business documents should not be uploaded until the organization’s policies, account type, sharing settings, and data-processing terms have been reviewed. Being source-based does not automatically make the tool suitable for every type of sensitive data.
Conclusion
NotebookLM’s strength is not a claim to be an “all-knowing AI,” but its ability to turn scattered material into a research space that can be queried and traced. It can be extremely efficient for users who regularly inspect citations, separate notebooks by topic, and put generated text through editorial review. For users who leave source quality and final verification to the tool, however, it may only produce mistakes more quickly.
Research sources
- https://support.google.com/notebooklm/answer/16164461?hl=en
- https://support.google.com/notebooklm/answer/16212283?hl=en
- https://support.google.com/notebooklm/answer/16296687?co=GENIE.Platform%3DAndroid&hl=en
- https://edu.google.com/ai-notebooklm/