- The most frequent NotebookLM failures are usually due to hidden limits, poorly prepared documents, and security filters when processing large PDFs.
- The audio summary function and certain advanced tools have temporary errors and limitations, especially in mobile versions.
- Defining a clear objective for each notebook and formulating specific questions significantly improves the quality of the answers.
- NotebookLM excels at working with your own documents, but it's best combined with other AI when you need up-to-date information from the web.
Entrusting the entire organization of our daily lives to a single AI tool might sound tempting, but it's a common mistake when starting to use NotebookLM . Google's platform is powerful, especially for students, researchers, and professionals working with large amounts of data, but it's neither magic nor infallible. Its behavior varies significantly depending on the type of file you upload, how you prepare it, and how you phrase your instructions.
In recent months, the community has identified a number of bugs, hidden limitations, and recurring misuses in NotebookLM that can completely disrupt your workflow if you're unaware of them. Some are related to technical issues with the system itself; others stem from misinterpretations of how you use the tool. Below, we've compiled the most common NotebookLM errors, their causes, and the most practical solutions or alternatives so you can get the most out of it without losing your mind every time something goes wrong.
Errors when uploading and interpreting documents in NotebookLM
One of NotebookLM's key features is its ability to read external sources such as PDFs, text files, videos, and audio files and convert them into a knowledge base . It is precisely in the uploading and processing of documents that some of the most frustrating problems for users arise.
A fairly common problem occurs when you try to upload a PDF file and the tool gets stuck loading indefinitely or returns an error message , even if the document meets the size limit shown in the interface (200 MB). From the outside, it seems like a random error, but it's almost always related to another, less visible restriction: the maximum number of words per font.
NotebookLM not only controls file size; it also imposes an approximate limit of 500.000 words per document . If you exceed this limit, the system may block the import or incompletely process the file. A practical solution is to split the original text into several smaller PDFs, ensuring that each fragment falls below this word threshold before uploading them back to NotebookLM.
Another frequently discussed problem is the apparent disappearance of pages or sections within an already loaded document . The user opens the notebook, requests information about a specific chapter, and NotebookLM responds as if that content doesn't exist. In many cases, this isn't a random bug, but rather a result of how the AI filters or interprets the file.
There are two typical causes: on the one hand, scanned pages without OCR or with unrecognizable text , which means that NotebookLM cannot actually "read" what is there; on the other hand, the activation of security filters on images or content considered sensitive , for example medical photos or anatomical illustrations in health notes, which can cause those pages to be ignored during processing.
To minimize this problem, it's advisable to scan the file beforehand with an OCR tool and ensure that the text is selectable and digitally readable . It's also a good idea to avoid, as much as possible, documents saturated with potentially sensitive images, or to split them to separate the problematic portion from the rest of the useful content.
A more subtle but highly relevant flaw for long projects is the "contamination" of the knowledge base when you mix too many different sources in the same notebook . As you add documents with different approaches or even contradictory information, NotebookLM sometimes generates responses that cross-reference data in a confusing way, or gets stuck on outdated information, even after incorporating more recent versions of the same text.
The best practice in this case is to work with separate notebooks for projects, clients, or topics that might conflict . Instead of concentrating everything in a single workspace, it's safer to create separate notebooks so that NotebookLM doesn't mix old and new sources in the same response, especially if you're handling sensitive documentation or something that requires absolute accuracy.
Common problems when uploading old or poorly scanned PDFs
Among the real-world user issues, there's a particularly illustrative case: someone tries to upload a PDF of a 19th-century book and keeps encountering the message "Error uploading the file. Try again." They try a clearer copy, compress the document to fit the size limit, and yet the tool still blocks the upload without further explanation.
Situations like this often involve a combination of factors: old, poor-quality scans, visual noise, non-standard PDF formats, or corrupted metadata . Even if the file appears readable at first glance, its internal structure may cause NotebookLM to reject it or fail to index it.
When you encounter a similar issue, it's advisable to run the document through a modern PDF editor that allows you to "clean" the file : save it again with a recent version of the PDF standard, apply OCR to all pages, remove unnecessary metadata, and, if necessary, split it into smaller sections. If the tool still rejects it after this, the best course of action is to extract the text to a plain text format (for example, .txt or .docx) and upload it as an alternative source , losing the formatting but ensuring that the AI can read the content.
It's also worth remembering that NotebookLM doesn't perform web searches or "search for" alternative copies of a book; it can only handle what you provide as a source. Therefore, it's crucial that the input material is as well-prepared and structured as possible before uploading it , especially when dealing with older works or historical scans.
Limitations and errors in audio summaries and podcasts
One of NotebookLM's standout features is its ability to turn your documents into a kind of podcast or personalized audio summary . This option, which usually appears in the interface as "Audio Summary" or similar formats, has gained a lot of popularity among those who prefer to review content while multitasking.
However, many users have noticed a drastic change in the behavior of this feature: audio files that previously could last up to 50 or 60 minutes are now reduced to pieces of only 20 or 30 minutes . This reduction not only affects the duration but also the depth of the analysis offered by the AI, which tends to skip relevant sections or, in the worst cases, "invent" parts of the content to fill gaps.
Google's technical team has acknowledged that this behavior is due to the side effects of internal software changes—in other words, an unintentional bug . In other words, there's nothing the user can configure to set a longer duration or force an extra level of detail; for now, it depends on how the model is performing at any given time.
While these functions are being stabilized, the only reasonable approach is to adopt a slightly more manual strategy: test the audio generation several times, review the result, and, if it falls short, supplement it with a direct reading of the document in your notebook . You can also ask specific questions after listening to the summary, requesting concrete clarification on chapters or sections that were covered too superficially.
It's important to keep in mind that engaging and smooth audio doesn't guarantee accuracy. In particularly concise summaries, the risk increases that AI will omit important nuances or creatively remix fragments . Therefore, in demanding academic or professional contexts, it's unwise to rely solely on podcasts and neglect to verify the information against the original source.
Interface flaws and differences between the web version and the app
Beyond how it processes data, NotebookLM suffers from several usability problems and limitations depending on the device it's used on . One of the most common issues arises in the mobile version, especially on Android, where many users discover that features present on the desktop are missing.
In the app or mobile web version, it's relatively common to find tools like internal notes, automatic quizzes, or flashcards missing . According to the Google team, this isn't a bug per se, but rather a "known limitation" of the mobile experience. Essentially, the full version of NotebookLM is designed to run from a desktop browser.
If you need to work intensively with complex notebooks, it's best to use the desktop web version whenever possible . On mobile, you can rely on quick reading or simple queries, but currently the app isn't designed to offer the same level of control or the same variety of tools as a computer.
Another frustrating aspect is the random interface glitches: notebooks that freeze, tabs that stop responding, or blank screens when navigating between fonts or trying to load large documents. It's striking that sometimes the service works flawlessly on an Android phone, but becomes almost useless on a MacBook or other desktop computer, even though other users with seemingly identical machines don't experience the same problem.
In these cases, the cause is usually external to NotebookLM: specific browser settings, extensions that interfere with the script, content blockers, or even local resource issues (RAM, GPU, etc.). A helpful first step is to try a different browser (Chrome, Edge, Firefox), disable any intrusive extensions, and check if the system is overloaded. If the error disappears when switching browsers or profiles, it was almost certainly not a direct NotebookLM problem.
On the other hand, server error messages continue to appear when generating study reports or creating questionnaires . These errors are usually intermittent and are due to peak loads on the generation infrastructure. There's little that can be done here beyond checking the internet connection, waiting a few minutes, and trying again. When the problem persists for hours, it's usually a good idea to consult official channels or forums to confirm if there's a widespread issue.
Bad usage habits that reduce the potential of NotebookLM
Not all problems stem from the technical side; many users fall into usage patterns that severely limit what the tool can do for them. One of the most typical is starting a notebook without a clear purpose . Documents are uploaded haphazardly, notes, reports, and articles are mixed together without a common thread, and then the AI is expected to produce an organized and coherent summary.
Before uploading anything, it's worth pausing for a moment to ask yourself: Do I want to research a specific topic, summarize extensive materials, or simply organize scattered ideas? With a defined purpose, you can better choose which documents to include, what kind of questions to ask, and which outputs (summaries, outlines, study guides, podcasts) make the most sense for that notebook.
Another common mistake is uploading poorly prepared documents: texts without structure, without clear titles or subtitles, endless paragraphs, and poorly separated sections . While NotebookLM can partially fix this mess, its performance improves significantly when the content already has a logical hierarchy, with descriptive headings and reasonably organized paragraphs.
It's also important to remember that NotebookLM doesn't perform its own internet searches . Everything it generates is based on the sources you provide, plus general knowledge of the model. So, if the documentation is incomplete or flawed in certain areas, the results are likely to be as well. Therefore, for serious research, it's advisable to double-check that you're actually uploading all the relevant material or just a very limited selection.
At the opposite extreme, there are also those who get excited about the sheer number of things NotebookLM can do with voice and audio, completely neglecting the quality of the source content. Converting poorly structured notes into a podcast doesn't magically make them clear ; if the source material is confusing, the resulting audio will be just as, or even more, muddled, no matter how well-narrated it may seem.
Therefore, a good habit is to dedicate some time beforehand to minimally organizing your PDFs, notes, and texts before uploading them , so that the AI has something solid to work with. From there, it makes sense to leverage its ability to generate outlines, review questions, scripts, and summaries that reinforce understanding.
How to ask NotebookLM better questions to avoid poor answers
Another major source of frustration has less to do with the technology itself and more with how users interact with it, for example, when customizing ChatGPT to improve responses . NotebookLM is designed to answer questions in natural language , but that doesn't mean it performs best with vague or overly general queries.
A classic example: simply asking “What is artificial intelligence?” within a notebook containing a book about AI applied to education . NotebookLM might respond with a very generic definition that doesn't utilize much of your specific sources. However, if you ask “What are the main applications of artificial intelligence in education according to this book?” you are guiding the model to the specific part of your documentation that interests you.
The more context and specificity you include in your question, the better the tool will be able to filter the information from your notebook and return something useful and actionable . It's about telling the tool what you want, but also from what perspective: comparisons, pros and cons, impact on a sector, a summary of a specific chapter, a list of key concepts, etc.
It also helps a lot to specify the desired format of the output: “Create a bulleted outline,” “Summarize in 5 key points,” “Generate 10 multiple-choice questions about Chapter 3.” NotebookLM has built-in functions for quizzes and flashcards, but suggesting a specific output structure usually improves the clarity of the final product, even if you later want to refine it manually.
Finally, it is worth accepting that interacting with NotebookLM is, to a large extent, an iterative process: it is not about launching a single giant question and waiting for the perfect text , but about refining the instructions in successive rounds, fine-tuning the request and correcting possible misunderstandings of the model with additional instructions.
When does it make sense to use NotebookLM and when to rely on other AI?
Although NotebookLM shares DNA with other Google models and indirectly competes with tools like ChatGPT, its user philosophy is heavily focused on working with your own documents . Understanding what it can—and can't—helps avoid unrealistic expectations and misdirected approaches.
NotebookLM shines when you need to analyze, synthesize, and reorganize large blocks of information you already have : manuals, lengthy reports, technical books, class notes, previous research, etc. If your challenge is to tame that volume of text and turn it into something manageable (outlines, summaries, study guides, audio scripts), the tool is a perfect fit.
However, when you're looking for up-to-date web information, recent news, product comparisons, or rapidly changing data , a model focused on internet search (like certain modes of ChatGPT or Gemini itself in its connected versions) is usually more suitable. NotebookLM relies primarily on what you provide it, rather than actively crawling the web.
It's also useful to combine approaches: you can use another AI to quickly locate the best sources on a topic and then upload those selected documents to NotebookLM to delve deeper into them through summaries, questions, and custom podcasts. This way, you leverage the best of each tool without expecting a single one to do absolutely everything.
In terms of personal productivity, NotebookLM can save you many hours of rote reading, but it remains crucial to maintain a critical perspective on its results . Checking answers against sources, identifying potential contradictions, and correcting details are all part of responsible use, especially if your projects have academic, professional, or legal implications.
Understanding NotebookLM's common shortcomings—word limits per file, issues with poorly scanned PDFs, mobile app restrictions, bugs in audio summaries, or poor question-formulation habits—puts you at an advantage: you can anticipate where it will get stuck, better prepare your documents, and manage your expectations . Used wisely, it ceases to be a capricious black box and becomes a powerful ally when working with complex information.
