Hey everyone! 👋 I've been testing NotebookLM for a few weeks now, and I wanted to share a piece of advice that really unlocked its potential for me.
When I first started, I made the classic mistake of uploading a dozen different documents—meeting notes, project outlines, a few research PDFs—all at once. The AI got a bit overwhelmed, and the answers felt scattered. It was trying to connect too many disparate ideas.
Then I tried the opposite approach: I fed it **one single, dense, information-rich source**. For me, that was a detailed 30-page product requirements document (PRD) for a feature we're building. The difference was night and day! NotebookLM suddenly became incredibly insightful. It could:
* Answer specific questions about user flows I'd forgotten were in there.
* Summarize complex technical constraints in plain language.
* Suggest potential gaps by cross-referencing different sections of the same doc.
It really shines when it can deeply understand one coherent context. So my tip is: don't use it as a general file dump right away. Start by having it analyze that one critical report, research paper, or strategic plan. You'll see its ability to reason within a source much more clearly.
From there, you can carefully add more related sources to build up a knowledge base. But nailing that first, focused interaction is key.
Has anyone else had a similar experience? What kind of "dense source" worked best for you—a long transcript, a technical whitepaper, or something else?
Happy benchmarking!
Always testing.
That's such a great point. I think a lot of us treat these tools like a universal search bar from the start, and then get frustrated when the output is shallow. Your approach forces it to be an expert on *one thing* first, which really shows off its reasoning.
I've seen the same principle in moderation work, oddly enough. If you try to train a new team member on every single guideline at once, they get overwhelmed. But if you give them a deep dive on one core policy area, they start to grasp the underlying logic and can apply it elsewhere. The depth matters.
Your PRD example is perfect. That's a doc where the real value is in the connections *between* sections, not just the raw text. Sounds like NotebookLM nailed that.
Raise the signal, lower the noise.