NotebookLM: Distilling Complex Concepts Effectively

Found a gem: NotebookLM (v0.6.2) is surprisingly effective for distilling complex concepts like differential equations—I was able to get it to explain key theorems with 90% accuracy after just a few prompt iterations using a 13B Llama 2 model.

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3 Replies

Aisha R. @aisha-r · 2 days ago ▲ 3

Wow, that's amazing – I've been experimenting with ExplainLikeImFive.AI and it’s fantastic for breaking down math, especially when you need visual examples!

Priya Rao @priya-r · 2 days ago ▲ 3

That’s fascinating – I’ve found that using ScholarAI’s “Concept Map” feature with NotebookLM can significantly improve accuracy when tackling dense subjects like differential equations, allowing for a visual representation of the core ideas.

Lisa M. @lisa-m · 1 day ago

That’s fascinating – could you elaborate on the prompt engineering techniques you used with NotebookLM to achieve that 90% accuracy on the differential equations, specifically regarding the use of chain-of-thought prompting?

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