Earlier this year, I began researching mitral valve surgery (specifically, valve repair vs. replacement) to help someone close to me. This experience introduced me to Retrieval-Augmented Generation (RAG) tools, which made navigating such a complex topic much easier. If you’ve ever wondered how to tackle a complicated subject and organize a large body of information, read on—I think you’ll find this approach fascinating and useful.
When Google released NotebookLM in July, I immediately uploaded my research (over 2,000 pages of article, book PDFs) to test it—and the results were helpful for two reasons:
- Natural Questions: I could ask complex questions like, “When does a patient stop being a good candidate for valve repair and need a replacement?” in a conversational tone, without needing search-friendly phrasing. This made exploring the topic more fluid and less mentally taxing.
- Source Links: Answers were embedded with direct links to source material, helping me pinpoint context-specific information with ease. For example, when I asked, “What happens if mitral valve repair is unsuccessful?” I could instantly trace relevant academic papers. It’s a level of granular, relevance-based exploration that would be nearly impossible with traditional methods.
In September, Google added an audio overview feature to NotebookLM, simulating a discussion between two fake human voices based on the content. I created a 16-minute podcast-style recording, and the result was surreal. You can play it below or click here.
Here are my takeaways from the generated audio conversation:
- Uncanny Realism: The simulated back-and-forth between the two voices was impeccable, with natural pauses, affirmations, and narrative flow. It’s hard to believe it’s synthetic—offering a glimpse into the future of voice-based AI.
- Creative Analogies: The LLM’s abstractions were impressively accurate. For example, it compared annuloplasty to ‘reinforcing a loose buttonhole’ (7:35 mark) and chordal transposition to ‘borrowing a support beam for a weaker bridge section.’ (8:15 mark) These analogies didn’t exist in the source material, showcasing the LLM’s ability to conceptualize ideas in relatable terms.
- Content Quirks: Some script choices felt off. For instance, at the 4:30 mark, the audio explained conflicts of interest, likely because it appeared frequently in the source material (from publishers like UpToDate). While statistically accurate, it didn’t fit the flow of the main topic. The script also repeated information and ended abruptly—issues a human editor wouldn’t miss.
- Questionable Usefulness: It’s hard to judge the audio’s overall utility. Having already immersed myself in the topic, I couldn’t assess whether it would make the subject clearer for a novice. If you listen to the audio, I’d love to hear your reaction.
AI tools like NotebookLM offer a new way to approach personal research. Whether you’re buying a house, choosing a career, or understanding a complex medical condition, these tools let you collect authoritative sources and transform them into a dynamic knowledge repository. Instead of searching for specific answers and facing information overload, you can create a synthetic ‘expert’ to chat with—or even listen to. It’s a remarkable shift in how we navigate complex topics, turning a daunting process into an engaging and empowering experience.
PS: I chatted with NotebookLM to get more analogies for the surgical techniques and it consistently came up with aligned, insightful comparisons. Eg. door frame for annuloplasty, suspension bridge cables for neochordae. The conceptual grasp of the model is truly fascinating.