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If you’ve listened to the podcast for a while, you might have heard our ElevenLabs-powered AI co-host Charlie a few times. Text-to-speech has made amazing progress in the last 18 months, with OpenAI’s Advanced Voice Mode (aka “Her”) as a sneak peek of the future of AI interactions (see our “Building AGI in Real Time” recap). Yet, we had yet to see a real killer app for AI voice (not counting music).Today’s guests, Raiza Martin and Usama Bin Shafqat, are the lead PM and AI engineer behind the NotebookLM feature flag that gave us the first viral AI voice experience, the “Deep Dive” podcast:The idea behind the “Audio Overviews” feature is simple: take a bunch of documents, websites, YouTube videos, etc, and generate a podcast out of them. This was one of the first demos that people built with voice models + RAG + GPT models, but it was always a glorified speech-to-text. Raiza and Usama took a very different approach:* Make it conversational: when you listen to a NotebookLM audio there are a ton of micro-interjections (Steven Johnson calls them disfluencies) like “Oh really?” or “Totally”, as well as pauses and “uh…”, like you would expect in a real conversation. These are not generated by the LLM in the transcript, but they are built into the the audio model. See ~28:00 in the pod for more details. * Listeners love tension: if two people are always in agreement on everything, it’s not super interesting. They tuned the model to generate flowing conversations that mirror the tone and rhythm of human speech. They did not confirm this, but many suspect the 2 year old SoundStorm paper is related to this model.* Generating new insights: because the hosts’ goal is not to summarize, but to entertain, it comes up with funny metaphors and comparisons that actually help expand on the content rather than just paraphrasing like most models do. We have had listeners make podcasts out of our podcasts, like this one.This is different than your average SOTA-chasing, MMLU-driven model buildooor. Putting product and AI engineering in the same room, having them build evals together, and understanding what the goal is lets you get these unique results. The 5 rules for AI PMsWe always focus on AI Engineers, but this episode had a ton of AI PM nuggets as well, which we wanted to collect as NotebookLM is one of the most successful products in the AI space:1. Less is more: the first version of the product had 0 customization options. All you could do is give it source documents, and then press a button to generate. Most users don’t know what “temperature” or “top-k” are, so you’re often taking the magic away by adding more options in the UI. Since recording they added a few, like a system prompt, but those were features that users were “hacking in”, as Simon Willison highlighted in his blog post.2. Use Real-Time Feedback: they built a community of 65,000 users on Discord that is constantly reporting issues and giving feedback; sometimes they noticed server downtime even before the Google internal monitoring did. Getting real time pings > aggregating user data when doing initial iterations. 3. Embrace Non-Determinism: AI outputs variability is a feature, not a bug. Rather than limiting the outputs from the get-go, build toggles that you can turn on/off with feature flags as the feedback starts to roll in.4. Curate with Taste: if you try your product and it sucks, you don’t need more data to confirm it. Just scrap that and iterate again. This is even easier for a product like this; if you start listening to one of the podcasts and turn it off after 10 seconds, it’s never a good sign. 5. Stay Hands-On: It’s hard to build taste if you don’t experiment. Trying out all your competitors products as well as unrelated tools really helps you understand what users are seeing in market, and how to improve on it.Chapters00:00 Introductions01:39 From Project Tailwind to NotebookLM09:25 Learning from 65,000 Discord members12:15 How NotebookLM works18:00 Working with Steven Johnson23:00 How to prioritize features25:13 Structuring the data pipelines29:50 How to eval34:34 Steering the podcast outputs37:51 Defining speakers personalities39:04 How do you make audio engaging?45:47 Humor is AGI51:38 Designing for non-determinism53:35 API when?55:05 Multilingual support and dialect considerations57:50 Managing system prompts and feature requests01:00:58 Future of NotebookLM01:04:59 Podcasts for your codebase01:07:16 Plans for real-time chat01:08:27 Wrap upShow Notes* Notebook LM* AI Test Kitchen* Nicholas Carlini* Steven Johnson* Wealth of Nations* Histories of Mysteries by Andrej Karpathy* chicken.pdf Threads* Area 120* Raiza Martin* Usama Bin ShafqatTranscriptNotebookLM [00:00:00]: Hey everyone, we're here today as guests on Latent Space. It's great to be here, I'm a long time listener and fan, they've had some great guests on this show before. Yeah, what an honor to have us, the hosts of another podcast, join as guests. I mean a huge thank you to Swyx and Alessio for the invite, thanks for having us on the show. Yeah really, it seems like they brought us here to talk a little bit about our show, our podcast. Yeah, I mean we've had lots of listeners ourselves, listeners at Deep Dive. Oh yeah, we've made a ton of audio overviews since we launched and we're learning a lot. There's probably a lot we can share around what we're building next, huh? Yeah, we'll share a little bit at least. The short version is we'll keep learning and getting better for you. We're glad you're along for the ride. So yeah, keep listening. Keep listening and stay curious. We promise to keep diving deep and bringing you even better options in the future. Stay curious.Alessio [00:00:52]: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, partner and CTO at Residence at Decibel Partners. And I'm joined by my co-host, Swyx, founder of Smol.ai.Swyx [00:01:01]: Hey, and today we're back in the studio with our special guest, Raiza Martin. And Raiza, I forgot to get your last name, Shafqat.Raiza [00:01:10]: Yes.Swyx [00:01:10]: Okay, welcome.Raiza [00:01:12]: Hello, thank you for having us.Swyx [00:01:14]: So AI podcasters meet human podcasters, always fun. Congrats on the success of Notebook LM. I mean, how does it feel?Raiza [00:01:22]: It's been a lot of fun. A lot of it, honestly, was unexpected. But my favorite part is really listening to the audio overviews that people have been making.Swyx [00:01:29]: Maybe we should do a little bit of intros and tell the story. You know, what is your path into the sort of Google AI org? Or maybe, actually, I don't even know what org you guys are in.Raiza [00:01:39]: I can start. My name is Raisa. I lead the Notebook LM team inside of Google Labs. So specifically, that's the org that we're in. It's called Google Labs. It's only about two years old. And our whole mandate is really to build AI products. That's it. We work super closely with DeepMind. Our entire thing is just, like, try a bunch of things and see what's landing with users. And the background that I have is, really, I worked in payments before this, and I worked in ads right before, and then startups. I tell people, like, at every time that I changed orgs, I actually almost quit Google. Like, specifically, like, in between ads and payments, I was like, all right, I can't do this. Like, this is, like, super hard. I was like, it's not for me. I'm, like, a very zero-to-one person. But then I was like, okay, I'll try. I'll interview with other teams. And when I interviewed in payments, I was like, oh, these people are really cool. I don't know if I'm, like, a super good fit with this space, but I'll try it because the people are cool. And then I really enjoyed that, and then I worked on, like, zero-to-one features inside of payments, and I had a lot of fun. But then the time came again where I was like, oh, I don't know. It's like, it's time to leave. It's time to start my own thing. But then I interviewed inside of Google Labs, and I was like, oh, darn. Like, there's definitely, like—Alessio [00:02:48]: They got you again.Raiza [00:02:49]: They got me again. And so now I've been here for two years, and I'm happy that I stayed because especially with, you know, the recent success of Notebook LM, I'm like, dang, we did it. I actually got to do it. So that was really cool.Usama [00:03:02]: Kind of similar, honestly. I was at a big team at Google. We do sort of the data center supply chain planning stuff. Google has, like, the largest sort of footprint. Obviously, there's a lot of management stuff to do there. But then there was this thing called Area 120 at Google, which does not exist anymore. But I sort of wanted to do, like, more zero-to-one building and landed a role there. We were trying to build, like, a creator commerce platform called Kaya. It launched briefly a couple years ago. But then Area 120 sort of transitioned and morphed into Labs. And, like, over the last few years, like, the focus just got a lot clearer. Like, we were trying to build new AI products and do it in the wild and sort of co-create and all of that. So, you know, we've just been trying a bunch of different things. And this one really landed, which has felt pretty phenomenal. Really, really landed.Swyx [00:03:53]: Let's talk about the brief history of Notebook LM. You had a tweet, which is very helpful for doing research. May 2023, during Google I.O., you announced Project Tailwind.Raiza [00:04:03]: Yeah.Swyx [00:04:03]: So today is October 2024. So you joined October 2022?Raiza [00:04:09]: Actually, I used to lead AI Test Kitchen. And this was actually, I think, not I.O. 2023. I.O. 2022 is when we launched AI Test Kitchen, or announced it. And I don't know if you remember it.Swyx [00:04:23]: That's how you, like, had the basic prototype for Gemini.Raiza [00:04:26]: Yes, yes, exactly. Lambda.Swyx [00:04:28]: Gave beta access to people.Raiza [00:04:29]: Yeah, yeah, yeah. And I remember, I was like, wow, this is crazy. We're going to launch an LLM into the wild. And that was the first project that I was working on at Google. But at the same time, my manager at the time, Josh, he was like, hey, I want you to really think about, like, what real products would we build that are not just demos of the technology? That was in October of 2022. I was sitting next to an engineer that was working on a project called Talk to Small Corpus. His name was Adam. And the idea of Talk to Small Corpus is basically using LLM to talk to your data. And at the time, I was like, wait, there's some, like, really practical things that you can build here. And just a little bit of background, like, I was an adult learner. Like, I went to college while I was working a full-time job. And the first thing I thought was, like, this would have really helped me with my studying, right? Like, if I could just, like, talk to a textbook, especially, like, when I was tired after work, that would have been huge. We took a lot of, like, the Talk to Small Corpus prototypes, and I showed it to a lot of, like, college students, particularly, like, adult learners. They were like, yes, like, I get it, right? Like, I didn't even have to explain it to them. And we just continued to iterate the prototype from there to the point where we actually got a slot as part of the I.O. demo in 23.Swyx [00:05:42]: And Corpus, was it a textbook? Oh, my gosh.Raiza [00:05:45]: Yeah. It's funny. Actually, when he explained the project to me, he was like, talk to Small Corpus. It was like, talk to a small corpse?Swyx [00:05:51]: Yeah, nobody says Corpus.Raiza [00:06:00]: It was like, a small corpse? This is not AI. Yeah, yeah. And it really was just, like, a way for us to describe the amount of data that we thought, like, it could be good for.Swyx [00:06:02]: Yeah, but even then, you're still, like, doing rag stuff. Because, you know, the context length back then was probably, like, 2K, 4K.Raiza [00:06:08]: Yeah, it was basically rag.Raiza [00:06:09]: That was essentially what it was.Raiza [00:06:10]: And I remember, I was like, we were building the prototypes. And at the same time, I think, like, the rest of the world was. Right? We were seeing all of these, like, chat with PDF stuff come up. And I was like, come on, we gotta go. Like, we have to, like, push this out into the world. I think if there was anything, I wish we would have launched sooner because I wanted to learn faster. But I think, like, we netted out pretty well.Alessio [00:06:30]: Was the initial product just text-to-speech? Or were you also doing kind of, like, synthesizing of the content, refining it? Or were you just helping people read through it?Raiza [00:06:40]: Before we did the I.O. announcement in 23, we'd already done a lot of studies. And one of the first things that I realized was the first thing anybody ever typed was, summarize the thing. Right?Raiza [00:06:53]: Summarize the document.Raiza [00:06:54]: And it was, like, half like a test and half just like, oh, I know the content. I want to see how well it does this. So it was part of the first thing that we launched. It was called Project Tailwind back then. It was just Q&A, so you could chat with the doc just through text, and it would automatically generate a summary as well. I'm not sure if we had it back then.Raiza [00:07:12]: I think we did.Raiza [00:07:12]: It would also generate the key topics in your document, and it could support up to, like, 10 documents. So it wasn't just, like, a single doc.Alessio [00:07:20]: And then the I.O. demo went well, I guess. And then what was the discussion from there to where we are today? Is there any, maybe, intermediate step of the product that people missed between this was launch or?Raiza [00:07:33]: It was interesting because every step of the way, I think we hit, like, some pretty critical milestones. So I think from the initial demo, I think there was so much excitement of, like, wow, what is this thing that Google is launching? And so we capitalized on that. We built the wait list. That's actually when we also launched the Discord server, which has been huge for us because for us in particular, one of the things that I really wanted to do was to be able to launch features and get feedback ASAP. Like, the moment somebody tries it, like, I want to hear what they think right now, and I want to ask follow-up questions. And the Discord has just been so great for that. But then we basically took the feedback from I.O., we continued to refine the product.Raiza [00:08:12]: So we added more features.Raiza [00:08:13]: We added sort of, like, the ability to save notes, write notes. We generate follow-up questions. So there's a bunch of stuff in the product that shows, like, a lot of that research. But it was really the rolling out of things. Like, we removed the wait list, so rolled out to all of the United States. We rolled out to over 200 countries and territories. We started supporting more languages, both in the UI and, like, the actual source stuff. We experienced, like, in terms of milestones, there was, like, an explosion of, like, users in Japan. This was super interesting in terms of just, like, unexpected. Like, people would write to us and they would be like, this is amazing. I have to read all of these rules in English, but I can chat in Japanese. It's like, oh, wow. That's true, right? Like, with LLMs, you kind of get this natural, it translates the content for you. And you can ask in your sort of preferred mode. And I think that's not just, like, a language thing, too. I think there's, like, I do this test with Wealth of Nations all the time because it's, like, a pretty c…
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