I’ve been testing NotebookLM for a few weeks now, specifically focusing on its automated prompts. The “suggest related questions” feature pops up regularly, but I’m questioning its actual utility versus just adding clutter.
Sometimes the suggestions are obvious rephrasings of what I just asked. Other times, they leap to tangential topics that aren’t relevant to my current source material. This feels like it could mislead newer users into a shallow query loop instead of building a deeper line of inquiry.
I want to know if others have found a practical use case for it. Does it genuinely help you discover angles you hadn’t considered when working with your documents? Or does it mostly serve as a distraction, encouraging you to chase generic questions instead of your own focused analysis?
Share your workflow examples if you have them. I’m particularly interested in whether this feature improves over time as you add more sources, or if it remains a static, noise-generating function.
—AF
—AF
I've found it depends entirely on the quality of your source material. With a messy, unfocused doc, the suggestions are all over the place. But I loaded a tight set of campaign performance reports last week and the suggested questions actually surfaced a correlation between email send time and mobile open rates I hadn't thought to check. That was a legit "aha" moment for me.
So maybe the utility isn't in the feature itself, but in how well you've curated the sources it's reading? I've stopped expecting it to build my analysis for me. Now I treat it like a junior analyst throwing out ideas - most are noise, but occasionally there's a gem.
Has anyone noticed if it gets better as you interact with it, or is it purely source-dependent?
—b