🧠 Beyond Information Chaos: NotebookLM Is Your New Advantage

If you’re a hoarder like me, you’ll have multiple browser tabs open, hundreds of YouTube videos in your “watch later” list, and an endless list of Mckinsey papers you “plan on reading someday”.

Every day my pile grows.

Because every day I find it. The gold nugget, the groundbreaking video, the greatest research paper ever to be written unlocking the universal secrets of AI and beyond.

And what do I do?

I save it.

With the best intentions I save it into the abyss. Never to be seen or thought of again.

Until this…

NotebookLM

You can think of NotebookLM as ChatGPT or Claude with one critical difference - you control the data on which it was trained.

It will:

• Process and store documents (PDFs, Google Docs, text, web links, YouTube transcripts, even audio files)

• Let you chat directly with those sources

• Create responses grounded in the information on which you decide (with citations to reduce AI hallucinations)

Now, instead of saving things to a million different locations, I upload everything to NotebookLM and it's available to me via an AI interface.

Not another notes app

"Yeah, yeah, yeah, Adam. It's just another notes app..." I hear you say.

And I get it. We're drowning in options for tools. But NotebookLM represents something much more significant: a practical implementation of Retrieval Augmented Generation (RAG).

Here's why it pays to understand RAG now:

RAG will fundamentally transform how businesses use AI in the coming years. Here’s what's happening under the hood:

1. Chunking: When you upload documents to NotebookLM, the system breaks them into smaller, meaningful segments – "chunks" of related information.

2. Vectorization: These chunks are then converted into numerical representations (vectors) that capture their semantic meaning. Think of it as creating a mathematical fingerprint for each piece of information.

3. Indexing: The system builds a searchable database of these vectors, making your information searchable.

4. Retrieval: When you ask a question, something fascinating happens. Your question undergoes the same vectorization process, transforming it into the same mathematical space as your documents. The system then measures the similarity between your question's vector and all the document chunk vectors – essentially finding which pieces of your information are mathematically closest to what you're asking. This isn't simple keyword matching; it's capturing the actual meaning of your question and finding semantically related content, even if the exact words differ.

5. Generation: Finally, the AI uses these retrieved chunks to generate an accurate, coherent response – grounded in your actual information.

This process is why RAG systems like NotebookLM deliver responses that are both relevant to your specific content and presented in natural language.

In the next 12-24 months, expect RAG systems to become the standard for handling organizational knowledge. Companies that implement these systems early will gain massive efficiency advantages – imagine every employee having instant access to all institutional knowledge, perfectly contextualized to their specific questions.

NotebookLM isn't just another tool – it's your preview of how professional knowledge work will function in the very near future.

Why will RAG be so transformative? Because it solves a problem that's been holding companies back for decades: the integration of siloed information systems.

Companies are reluctant to upgrade legacy systems because it's prohibitively expensive and disruptive. RAG changes this equation entirely. Instead of replacing those systems, RAG lets you extract value from them exactly as they are. It sits as a layer on top of your existing infrastructure – connecting your ERP, CRM, document storage, email archives, and internal wikis without requiring migration or replacement.

This means the knowledge trapped in your decade-old database becomes just as accessible as information in your newest cloud platform. Your employees don't need to learn five different interfaces or remember which system holds which information. They simply ask questions in natural language and get coherent answers drawn from across all your systems.

NotebookLM gives you a taste of this future while at the same time being a handy repository for your own world.

Ok, what else does NotebookLM do?

I'm glad you asked:

1. It creates AI podcasts

I love this for travel and commuting. Google explains: 'Audio Overviews are lively "deep dive" discussions between two AI hosts who summarize key topics in your sources.' Download the audio to listen anywhere (or use the mobile app which will release on the 20th of May), focus on specific topics, and digest information on the go - the results are really good.

2. It transforms documents into other formats

Generate practical outputs with a single click:

• Study guides with questions and answers from your material

• Timelines that visualize project stages or historical events

• Briefing docs perfect for onboarding or project summaries

• FAQs play a similar interview role helping you understand your information even deeper

3. It will discover new sources for you

NotebookLM now has a perplexity style source discovery feature that will find sources for you should you be lacking in information to upload.

How to use it effectively

For one, NotebookLM is the backbone of this newsletter. It helps me search, digest, and understand the world of AI better. It helps me with study, client projects, and my golf swing.

Here are a few other ideas to get you started:

• Research Enhancement: Upload research papers, then ask specific questions. Get answers backed by citations from your actual sources - I wish I had this at university.

• Meeting Note Transformation: Upload meeting recordings and get transcripts you can query and summarize. Transform how you capture and use meeting information.

• Efficient Document Analysis: Upload reports, contracts, or financial documents and extract what matters. Ask "What are the three biggest risks mentioned in this contract?" and get precise, cited answers.

• Content Creation Assistant: Upload your scattered notes and research, then ask NotebookLM to generate drafts, outlines, or summaries based on that content. Turning a mess of raw information into structured documents.

• Mobile Learning: Convert dense documents to audio and listen during your commute. Absorb the key points of a lengthy report while on the go.

The Limitations You Should Know

NotebookLM isn't perfect. Be aware of these constraints:

• No Excel or CSV support (yet)

• The interface can be unintuitive at times

• Chat history disappears if you close your browser (save important insights as notes)

• Currently only available in English (though audio overviews in different languages is currently in Beta)

• As with any Google product, long-term availability isn't guaranteed. Download important content.

And the last one is more a warning than a limitation but please ensure what you're uploading to Notebook LM isn't sensitive company information. It helps to remember Notebook LM is a Google product therefore if you wouldn't email the document to or from your Gmail account, you probably shouldn't upload it to Notebook LM.

The Bottom Line

Today, I still hoard browser tabs, YouTube videos, and research papers. But now I hoard them in NotebookLM, where they're a lot more useful.

AI-powered hoarding? I didn’t see that one coming.

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From around the AI traps

• Microsoft Copilot Vision launched, allowing your AI assistant to actually see what's on your screen and provide contextual help while browsing - just imagine the productivity gain (and privacy questions).

• LinkedIn research reveals India (62%) and US (51%) dominate AI adoption while SMBs fall drastically behind big enterprises - despite SMBs representing 90% of global businesses and 50% of workers worldwide.

• Apple sent Google's stock plummeting by testifying they're "actively looking" at AI search options like Perplexity and Anthropic for Safari - potentially ending the $20B Google search deal.

• Sam Altman and Elon Musk are racing to build competing "everything apps" - with Musk pushing X's payment features while Altman's World app uses controversial iris-scanning to verify humans in an AI age.

See you next week,

Adam