I’ve been experimenting with Google NotebookLM for about a month now and I’m really impressed with what it can do!
For those who haven’t tried it yet, it’s Google’s AI tool that lets you upload documents and then have conversations about their content. Pretty cool concept and the execution is solid.
I’m really interested in hearing how others are using this tool. I feel like there are probably tons of creative applications I haven’t thought of yet.
Currently I’m mainly using it to break down complex academic papers and get simplified explanations. It’s great for that but I know there’s more potential here.
Questions I have:
- Anyone experimenting with creative writing or screenplays?
- How well does it work for professional documents?
- What file formats give the best results besides PDF?
- Any tips for crafting better prompts?
Also wondering about:
- Performance compared to other AI tools for document analysis?
- Any limitations or bugs you’ve encountered?
- Creative workflows you’ve developed?
I heard someone mention using it for tabletop RPG worldbuilding which sounds incredible but I’d love more specifics on how that works.
Share your experiences please! Whether you’re a power user or just getting started, I’m eager to learn from your experiments. This tool seems like it has huge potential once we discover all the different ways to leverage it effectively.
Using NotebookLM for code documentation analysis has been a game changer at work. It handles technical specs and architecture docs quite well.
A good workflow I’ve found is uploading related documents together—like requirements, design docs, and meeting notes. Then, I ask it to find inconsistencies or gaps among them. This has helped me catch several issues before they became major problems.
When it comes to file formats, I’ve noticed Word documents work better than PDFs. PDFs can mess up the formatting and tables. Markdown files are also a great option.
For prompts, avoid general questions. Instead, try asking it to “explain this as if you’re onboarding a new engineer” or “what should someone know before modifying this system”. This approach yields much more practical responses.
I’ve also used it for retrospectives. Uploading all incident reports from a quarter and asking it to identify patterns has been quite eye-opening.
One limitation I’ve encountered is that it struggles with very large codebases or documents that exceed size limits. Additionally, it sometimes hallucinates details that aren’t in the source material, so it’s wise to double-check anything critical.
In comparison to ChatGPT or Claude, it may be less creative, but it excels at staying focused on the documents instead of spouting external knowledge.
I’ve been using it for meal planning and recipe tweaks - sounds weird but stick with me. I upload cookbooks or recipe collections, then ask things like “what can I make with leftover chicken and these pantry staples?” or “how do I modify these for diabetes?” Way better than endless food blog scrolling, and it pulls from your actual uploaded recipes instead of random internet stuff.
NotebookLM is fantastic for legal document review and contract analysis. I work in procurement and constantly upload vendor agreements, compliance docs, and regulatory guidelines together. The AI’s great at spotting conflicts between contract terms or catching where vendor proposals miss our compliance requirements.
My go-to workflow is due diligence packages. I’ll upload financial statements, audit reports, and company presentations, then ask specific questions about discrepancies or red flags. It’s caught several inconsistencies that would’ve taken me hours to find manually.
For prompts, be super specific about perspective. Don’t ask “what are the risks here” - ask “what would concern a compliance officer about these terms” or “identify clauses that could create operational problems.” This contextual framing gets you way more useful insights.
File format tip: plain text exports from legal databases work much better than the original PDFs. It handles structured text way more reliably than complex formatting.
One big limitation though - it struggles with redacted documents. Sometimes tries to guess what’s behind the redactions, which obviously isn’t helpful for sensitive legal work.
Been testing NotebookLM for research synthesis and it’s pretty solid.
I throw all my research in there - industry reports, competitor analysis, user interviews, market data - then ask it to find conflicting viewpoints or make executive summaries. Saves hours of manual cross-referencing.
The tabletop RPG thing is legit. Help a friend who runs campaigns and we upload world lore, character backstories, session notes, then ask stuff like “what would this NPC know about recent events” or “create plot hooks based on last session’s player actions”. Works surprisingly well for keeping complex stories consistent.
For creative writing, it’s decent at analyzing existing drafts. Upload chapters and ask it to track character development or spot plot holes. Not great for generating new content but solid editing feedback.
One trick - when working with technical docs, ask it to create different views of the same info. Like “explain this system architecture for a PM versus a junior dev”. Really helps when presenting the same concept to different audiences.
Biggest limitation is dense technical diagrams in PDFs. It can describe what it sees but misses nuanced relationships between components. Text-heavy documents work way better than visual ones.
I use it mainly for coordinating research across multiple projects. I’ll dump journal articles, conference papers, and my draft manuscripts into focused notebooks for each research area. The best part? I can ask it to spot methodological gaps or show me where my work connects with existing literature. Way faster than doing traditional lit reviews. One thing I didn’t expect - it’s great for grant proposals. I upload funding guidelines with my research summaries and project descriptions, then ask it to flag alignment issues or what I’m missing. This has helped me fix several proposals before sending them out. Performance-wise, it’s better than general AI tools at keeping context across multiple documents. But it sometimes chokes on mathematical notation and complex stats in research papers. At least the citations always come from what I’ve uploaded, so I trust the accuracy. Couple tips: organize your documents by date or theme in notebooks - it improves responses. And ask follow-up questions instead of cramming everything into one query. You’ll get way more detailed insights that way.