I Tested NotebookLM with Big Docs and Long Videos—Here's What Happened

Exploring the Limits of NotebookLM: A Deep Dive into Its Capabilities

NotebookLM has revolutionized how we interact with vast amounts of information. However, the true test of any tool lies in its ability to handle extreme scenarios. To understand just how robust NotebookLM is, I decided to push it beyond its usual limits by uploading massive documents and long YouTube videos—far exceeding typical use cases. This journey revealed both its strengths and some limitations.

Understanding Tesla’s Sustainable Energy Impact Report

One of the first tests involved a 42-page document titled “Tesla’s Sustainable Energy Impact: 2024 Report.” This wasn’t a simple text file; it was a dense report filled with text, charts, images, and graphs that illustrated energy consumption and production trends. The upload process was smooth, and NotebookLM quickly generated a summary.

I asked several questions, such as: - What are the main areas of focus for Tesla’s sustainable energy initiatives in the coming years? - What were the primary sources of greenhouse gas emissions mentioned in Tesla's operations?

In both cases, NotebookLM provided relevant answers. However, when I asked about specific data, like how many metric tons of CO2e Tesla customers avoided releasing in 2024, it failed to highlight the correct answer of 32M (a 60% increase from 2023). This showed that while NotebookLM can handle general queries, it may struggle with precise numerical data extraction.

Reading Apple’s Earnings Call via NotebookLM

After being impressed with how NotebookLM handled the Tesla report, I wanted to see if it could manage multi-document tasks. I gathered transcripts from Apple’s last three quarterly earnings calls, which are lengthy and detailed discussions about performance metrics and future outlooks.

I created a new notebook, added these PDF files, and started asking questions. For example: - What were the key revenue figures and growth drivers for Apple in the past three quarters?

NotebookLM provided key figures along with sources. When I asked about Apple’s strategy for “Apple Intelligence,” it delivered a detailed answer regarding their phased rollout, hardware integration, and key features. The ability to click on sources and refer back to the exact paragraphs in the uploaded documents was particularly useful.

Even when I asked about the outlook for the next quarter, NotebookLM pulled relevant information from previous reports and shared key findings. This demonstrated its capability to synthesize insights from multiple documents.

Learning Self-Hosting with Educational YouTube Videos

I often come across long-form educational content on YouTube, but watching a 3-hour or 4-hour video is time-consuming. To bypass this, I used two Kubernetes-related YouTube videos—each around four and three hours long. I copied and pasted the links into NotebookLM, and they were listed as active sources.

I asked: - Can you explain the core differences between a Deployment and a StatefulSet?

NotebookLM provided a quick and accurate response. When I asked about best practices for securing a Kubernetes cluster, it pulled relevant information from both videos, stitched them together, and displayed the answer. This highlighted NotebookLM’s ability to extract and combine knowledge from different sources.

The Limitations of NotebookLM

While NotebookLM excels with text-heavy PDFs and YouTube videos, its accuracy can be inconsistent when dealing with large PDFs containing many images and graphs. Google itself advises users to double-check responses, indicating that there are still areas for improvement.

Confessions of a NotebookLM Abuser

Pushing NotebookLM to its limits revealed several hiccups, but overall, it performed well. While it has limitations in terms of the number of sources it can handle, these are generous enough for most workflows. With its ability to manage large volumes of information, it’s possible to create an “Everything” notebook in NotebookLM to tackle information overload without breaking a sweat.