Today, I’m joined by Wyndo Mitra, author of the AI Maker blog. In this episode, we’re going to show you how to harness these tools to build your very own research assistant! We’ll dive into using NotebookLM, integrating it with Google Gemini to create stunning visuals, and actually using it for targeted research to boost your productivity and create high-quality outputs.
🎧 You can also listen directly to the episode on YouTube, Spotify or Apple Podcasts.
What to Expect
→ How to use and combine NotebookLM (NLM) and Google Gemini as a research assistant
→ How to add and manage different types of sources (articles, YouTube videos, PDFs) in NLM
→ Techniques for summarizing, extracting insights, and organizing information from multiple sources
→ Using NLM’s podcast feature to generate and listen to custom audio summaries
→ How to interact with AI-generated podcasts for personalized learning
→ Methods for conducting competitor analysis and opportunity mapping using these tools
→ Steps to visualize research findings and data using Gemini, including infographics and charts
→ Tips for prompting Gemini to create specific types of visualizations
→ How to use NLM’s Discover feature to find and segment relevant research sources
→ The benefits of controlling your data sources to reduce AI hallucinations and improve accuracy
→ Practical applications for business, such as preparing presentations, analyzing financials, and maintaining privacy with sensitive data
The Key Takeaways
→ Notebook LM and Google Gemini are powerful, underrated AI tools for research and productivity.
→ Combining Notebook LM and Gemini allows you to build a custom research assistant without extra subscriptions.
→ Notebook LM reduces hallucinations by only using information from sources you provide (articles, PDFs, YouTube, etc.).
→ You can add multiple types of sources and extract insights, summaries, or tables from them.
→ The podcast feature in Notebook LM lets you generate and listen to custom audio summaries, and even interact with the AI for personalized learning.
→ Gemini is especially useful for visualizing data and research findings, such as creating infographics and charts from Notebook LM outputs.
→ Prompting Gemini with specific requests can produce high-quality, presentation-ready visuals.
→ The Discover feature in Notebook LM helps you find and segment relevant sources (PDFs, YouTube, forums) for focused research.
→ This workflow is ideal for business use, especially when privacy is important, as you control all input data.
→ Using these tools together improves research accuracy, reduces misinformation, and streamlines the process of turning data into actionable insights.
Timestamps
(00:00) Intro
(01:08) Why NotebookLM/Gemini is underrated
(02:45) NotebookLM’s interface and benefits
(04:08) Adding sources to NotebookLM
(06:01) Using the chat feature to summarize/segment info
(07:19) Generating an AI podcast from sources (and also join in)
(12:02) Use case: Research and compare PM tools
(14:01) Use case: create pros/cons analysis
(17:26) Combining NotebookLM with Gemini to create visuals
(21:06) Gemini: prompting for specific charts, infographics
(24:47) NotebookLM’s NEW Discover feature
(27:51) Use case: Demo Nvidia’s financials and visualizing data
(31:08) Advantages of NLM and Gemini workflows
(34:20) Final thoughts
(37:25) Outro
Follow Wyndo
→ LinkedIn: https://www.linkedin.com/in/wyndomitrabuwana/
→ X/Twitter: https://x.com/wyndomb
→ AI Consulting: https://www.aiwithwyndo.com/
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Thanks for reading, and see you next week!
Barry.
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