Скачать или смотреть Optimize Your Document Analysis Workflow with Notebook LM

I've been testing NotebookLM for document analysis.

Wanted to share what I found because this thing is different from ChatGPT, Claude, and Gemini in one critical way.

I used a recruiting example for this test because I have recruiters in my network. But this applies to anyone who needs to analyze multiple documents quickly.

Here's what makes NotebookLM different.

When you use ChatGPT or Claude, they pull from their massive training datasets plus your input. That's powerful, but it can lead to hallucinations or answers that aren't purely based on your specific documents.

NotebookLM only uses what you upload. Nothing else.

It won't make stuff up from the internet or its training data. It analyzes only your documents and gives you answers based solely on that content.

So I ran a simple test.

Created 10 fake CVs with different skill sets and experience levels. Wrote a job description with specific requirements. Uploaded everything to NotebookLM.

Then I asked it to rank the candidates based on how well they matched the job requirements.

It gave me a ranked list in seconds.

Showed me exactly why each candidate was positioned where they were. Pulled specific details from the CVs that matched or didn't match the job description. No guessing, no external data, just pure analysis of what I uploaded.

But here's where it gets interesting.

This isn't just for recruiting. You can use this same approach for tons of other workflows.

Got 50 customer feedback responses? Upload them and ask for common themes or pain points.

Analyzing competitors? Drop in their marketing materials and ask for positioning differences.

Reviewing vendor proposals? Upload all of them and ask which ones meet your criteria best.

Drowning in meeting notes? Upload a month's worth and ask for action items or recurring topics.

The workflow is dead simple.

Upload your documents. Ask a question. Get an answer based only on what you provided.

And it's completely free at notebooklm.google.com.

I'm running more AI workflow experiments this week and sharing what actually works versus what's just hype.

If you're analyzing documents manually right now, this might save you hours.

What documents are you analyzing regularly that could benefit from this approach?