Is NotebookLM Really a Game-Changer?

Hey fellow cyborgs!

I thought I might dive into Google's Notebook LM a bit since there seems to be a lot of buzz around it.

Despite its hype, I've found it doesn't quite match up to my go-to tools, Boodle and Mem. However, its impact in education, especially for creating podcasts from class materials, is noteworthy.

I've been experimenting with structured microcontent using Simplebase, a beta program where students craft structured notes that can be used as a reference for a custom AI.

We focused on cultural insights from Erin Meyer’s The Culture Map and Polish culture, aiming to create a searchable database. Students choose a “concept” or “thing” and write notes targeting students or business people visiting Poland.

For example every cultural element note has the following sections:

1. Definition

2. Explanation

3. Example

4. Key questions

5. References

6. Related terms

So I thought I would compare the this custom bot with NotebookLM in this video by transferring those notes into NotebookLM.

Key Takeaways

My experimentations show that NotebookLM is actually behind other more useful tools, except for its impressive podcast generator (something I personally have little use for).

Here is what I found:

1. Structured Knowledge is Crucial: Notebook LM’s performance improves significantly with structured input. When documents like the Commisco Poland insight report are well-organized, the AI references specific sections effectively, enhancing its ability to recall information in the knowledge base.

2. Contextual Understanding Limitations: While NotebookLM can transform structured notes into content like LinkedIn posts, it struggles with contextual understanding and linking related concepts, unlike more general models like ChatGPT.

3. Tool Selection Matters: Despite Notebook LM’s features, tools like Boodle offer superior capabilities for structuring knowledge and fostering collaboration, making them more effective for integrating AI into educational and content creation workflows.

For a deeper dive into these insights and to see practical demonstrations, be sure to watch the full video!

Have you tried NotebookLM? What are your experiences?