I arrived into generative AI with a skeptic’s eyebrow that will forever be eyebrow-raised. Without guardrails, chatbots have a habit of fabricating facts, a phenomenon that researchers at Stanford HAI and beyond have documented. NotebookLM overcame this suspicion for me, because it requires the model to base itself on sources that I pick, and reveals them. That one design decision makes all the difference.
After several months of testing, it now accompanies me every day, juggling health education, pain-sympathetic workout routines, gaming research and planning home lab projects. Here is how I make it pull its weight — and where it still falls short.
Why this AI finally clicked
NotebookLM allows me to create a private corpus — PDFs, docs, research papers, wiki pages — and then query that material. The service produces summaries, citations and even an Audio Overview, so I can listen to the highlights as I fix my coffee. Also, I rely on the mind map view to see concepts, dependencies and visualize them before drilling down.
Since responses come from a place of groundedness, I spend more time refining prompts than second-guessing them. When it does err, the citation trail makes it trivial to verify claims or to prune weak sources.
Plain-English health explainers people trust
I have chronic conditions, including fibromyalgia and migraine. And these are frequently misinterpreted: while the C.D.C. estimates that around 2 percent of adults are living with fibromyalgia, and the Global Burden of Disease ranks migraine among the leading causes of years lived with disability. That chasm between lived experience and public perception is huge.
To fill it, I constructed notebooks from reliable sources — pages from the NIH, Cochrane reviews and articles from clinical journals. NotebookLM translates that stack into something I can share with family: plain‑English explainers. I will produce an Audio Overview in a second language for relatives who don’t speak English as their first language, then spend some time on the script and citations before sending. It’s not medical advice, but it is accurate, digestible context that hacks through the weeds of social‑media myth.
Light-weight routines that adjust with pain and time
Exercise is like a moving target when you have waxing-and-waning pain. On good days, I’m up for so little as a half an hour walking; on flare days a minute on a foam roller feels herculean. I made a notebook filled with expert materials — notes from the American College of Sports Medicine on low‑impact exercise for chronic pain, say — as well as trusted yoga and physio materials.
Next I want “a 10‑minute sequence for neck and upper‑back tension at low energy,” or “three poses to downshift after a migraine aura.” NotebookLM Constructs pairs of options with lengths and cues based on my sources. Caveat: there are no video citations (no timestamps), and no in-line images, so I’ll have to manually seek out a pose name if I want to look at the picture. That takes a little longer, but it’s faster and more uniformly thorough than skimming through a dozen tabs.
Taming game guides and mods without 20 open tabs
I’m currently wasting time in Ark: Survival Evolved, where some of the taming is downright mystical. I fed NotebookLM a filtered mash of wiki entries and approved community guides, and then quizzed it with edge cases — weather, bait interactions, multi‑step setups. So instead of pausing through twenty-minute videos and darting around forums, I receive a tightened, source‑linked plan. Greatest with the quirk tames that there is no consensus on advice.
Good projects for home lab planning
I have been self‑hosting services on a NAS and fiddling with a couple of Raspberry Pi builds. Each project is spread over multiple docs, blog posts, and forum threads, and the order of operations is important. I maintain a notebook per project — Pi imaging, containers, reverse proxies, storage layouts — and work forwards from official documentation and a few reliable guides.
NotebookLM lets me stage things-to-do: leanings, watch-out-fors, and a checklist I can export. Industry reports, like those from Stack Overflow or Stripe, have chronicled for years how much time developers sacrifice to scattered documentation; rounding up sources in one place cuts down on my context‑switching. I still read the docs end‑to‑end, but I have a dependable map before I ever go.
Where NotebookLM falls short
Summaries risk sounding formulaic even with tailored prompts, and passages with multimedia citations require more fidelity, as with timestamps for videos and optional thumbnails for exercises. I would also love to see harder per‑section cites within long answers to speed verifications up.
On mobile, the app languishes behind the web in navigation and source control. My—petty, I’ll admit, but effective for me—workaround is to curate sources on my desktop and then use my phone for quick queries and audio recaps. And out of privacy concerns, I don’t upload anything sensitive, I just use public research and my de‑identified notes.
The bottom line
What earns a Place in NotebookLM to my day because it’s anchored: I bring the knowledge base, it turns receipts, and the tools—summaries, audio, mind maps—help me make my way from clutter to clarity. It’s not a reading substitute, but it transforms piles of sources into something I can take action on — whether I’m explaining a diagnosis, dampening a flare, taming a digital phoenix or wiring a home lab.
