Upskilling is the need of the hour as AI starts taking over most tasks you'd accomplish on a laptop, but there's no rules that prevent me from using AI for said skill acquisition. However, my fundamental concern with the process lay in the evaluation—my newly acquired skill would be put to test in the real world right away, or an academic examination if you're a student. I sought a low-consequence test methodology with few variables in the evaluation phase to see if NotebookLM is any good at learning a new skill.

Armed with past successes in mastering AutoHotKey and PowerToys optimization, I decided to test Google's AI for academicians with a slightly subjective yet personal experiment like brewing a good pour-over coffee. I’ve come to believe that the key to rapid, effective upskilling isn’t more information, but less. It’s about finding a handful of quality sources and having a way to intelligently process them. This is where AI tools like NotebookLM are changing the game. I’ve always been terrible at making coffee, but a friend suggested my electric drip coffee machine might be the culprit and manual pour-over might be the best remedy. I sought NotebookLM's assistance to save me hundreds of hours of experimentation and internet research for the perfect cup. Here's how it went.

The sour beginning

Selling the machine and going manual

My journey into home-brewed coffee started and, for a long time, ended with a generic drip coffee maker. The ubiquitous, automated gadget I thought was purpose-built to deliver good coffee. However, it always fell short no matter what I did. The pot was always acidic, and tasted vaguely burnt, like a caricature of what coffee is supposed to be. It treated expensive, locally roasted beans with fancy flavor notes on the bag and generic supermarket grounds with equal disdain, churning out a consistently terrible brew. Eventually, I gave up and sold it, resigning to instant coffee and weekly café splurges.

A similarly caffeinated friend suggested I give pour-overs a shot, explaining how the process is manual, and hence totally in my control. He vowed that manual extraction would improve the taste and it wasn't even expensive to begin with. Unwilling to be burnt twice, I thought this would make a fine test for NotebookLM as I go into this skill blind, with good cafe-tier coffee being the only metric of success. By using the same pre-ground beans that my old machine rendered rancid, I created a control group of one. If the coffee tasted good, it would be almost entirely due to the process I learned from the AI.

I was going to do my research, but smartly. I didn't want to spend hours binging YouTube videos, trying to separate genuine advice from sponsored content and marketing fluff disguised as brewing guides. I wanted a more direct, scientific approach to coffee.

Instead of asking a generic AI for synthesized answers pulled from the entire internet, I wanted to control the source material and NotebookLM was perfect for that. I found a few highly-regarded articles and posts on the /r/pourover subreddit, uploaded them to the AI, and started quizzing it in the chat interface.

A personal coffee tutor emerges

Asking the right questions

My first question was straightforward: "Based on the sources, what is the absolute essential equipment I need to start making pour-over coffee?" NotebookLM generated a list that included a dripper, filters, a gooseneck kettle for controlled pouring, a digital scale for precise measurements, and a timer. This is where the interactive part became crucial. I followed up, asking it to help me narrow the list down to the bare essentials. The AI confirmed only the dripper and paper were essential purchases, and I could substitute the gooseneck kettle with a steady hand. A smartphone stopwatch can stand in for a timer and a fixed-size scoop would eliminate the need of a scale. That last bit was overreach as I later discovered one must measure the water by weight too.

Next, I started digging into the fundamentals—the things my old drip machine completely ignored. The sources stressed that water should be just off the boil (around 90-96°C or 195-205°F), not boiling hot, to avoid scorching the grounds. While I was using pre-ground beans for this test, the AI made it clear that grinding fresh is a game-changer for flavor. I noted this point for future improvement. A crucial step I would have certainly skipped as a novice, NotebookLM explained that rinsing the paper filter with hot water before adding the coffee grounds removes any papery taste and pre-heats the dripper and carafe.

I felt like I was already miles ahead of where I would've been with manual research. The AI also provided a ready-to-use ratio of coffee to water, suggesting a range between 1:15 and 1:17. I asked it to do the math for me: "How much water do I need for 20 grams of coffee to make two cups?" It instantly replied with a target of around 320 grams of water. Thankfully, 1ml weighs about a gram, so I could use a graded kitchen flask.

Pouring my first cup

And the much-awaited taste test

For the pour technique, I've seen baristas go slow and circular, but NotebookLM explained the "bloom," the initial pour of a small amount of water to saturate the grounds would ensure even extraction. The AI also taught me not to pour water directly onto the filter walls, as it would bypass the coffee bed and result in a weak, under-extracted brew. When I asked why I should start pouring in the center and spiral outwards, the AI synthesized an answer from my sources: starting in the middle ensures the core of the coffee bed is saturated first, and spiraling outwards promotes an even extraction across all the grounds, preventing "channeling" where water finds a single path through and ignores the rest of the coffee.

Finally, there was the timing. I had no idea you could control the brew time. My sources recommended a total time of about two to three minutes. This brought me to the final variable: the grind. I told the AI that my beans had previously tasted bitter and acidic. Based on the provided texts, it explained the relationship between grind size and extraction. The AI’s advice helped me understand that I needed to manage my pour rate carefully to stay within that three minute window, ensuring I didn't make another bitter cup.

With my newfound knowledge, I set up my cheap plastic dripper over a carafe, rinsed the filter, added my two scoops of notoriously bad coffee, and started the process. I bloomed the grounds, watching them bubble up. I started my slow, steady pour, spiraling from the inside out. I watched the timer on my phone, adjusting my pour to hit the three-minute mark.

The resulting cup of coffee looked and smelled completely different from the sludge my old machine produced. I braced for that now-nonexistent acidic bite. Instead, the coffee was surprisingly smooth. There was a depth of flavor I’d never tasted from these beans before, a world away from the rancid brew I had started with. This was definitive proof that technique, not just the beans, was a massive part of the equation. Brewing two cups' worth of coffee also allowed me room for error.

NotebookLM is great for learning a new skill

Once again, I felt the benefit of using NotebookLM with my curated sources. My learning wasn't distracted by a dozen different ratios or techniques. I was given one solid, community-vetted method, and it worked. The AI gave me guidance like a friend, unbiased and objectively oriented to ensure a good pour. Importantly, it reinforced my faith in learning from limited sources instead of the confusion that generic AI, hours of YouTube could bring. From here, honing my technique by experimenting with grind size, ratios, and different beans will be an exciting process of refinement rather than a frustrating shot in the dark.