Overview
This lecture introduces Notebook LM, an AI-powered research assistant designed to organize, interact with, and extract insights from user-uploaded documents. It covers key features, use cases, limitations, and practical tips for maximizing its benefits.
Structure & Purpose of the Program
- The session has three parts: initial hands-on with Cong, practical guidance from Yusuke and Linh or Mai Anh, and a final interactive segment.
- The aim is to enable participants, especially newcomers, to quickly gain practical skills with Notebook LM.
Introduction to Notebook LM
- Notebook LM is an AI tool that analyzes only user-provided documents, not the whole internet.
- Supported files include PDFs, web links, YouTube videos, audio, Google Docs, and Slides.
- Emphasizes accuracy and trust by sourcing answers strictly from uploaded content, reducing misinformation.
- Limits: Does not add information beyond provided sources; quality depends on uploaded data.
Core Features & Functionalities
- Interactive chat allows summarizing, comparing, and querying specific document content.
- "Pinning notes" lets users save important AI responses and add personal reflections.
- Mind Map feature auto-visualizes connections between main ideas.
- Audio feature enables mini-podcast-style discussions; users can interact live with AI voices.
- Shareable notebooks, with varying levels of access control depending on the plan.
- Multi-language support and automatic translation.
Usage Limits & Versions
- Free version: Up to 100 notebooks, 50 documents per notebook, 50 questions, and 3 audio files.
- Plus (paid) version: Up to 500 notebooks, 300 documents per notebook, more advanced features.
- Maximum document size and word count apply; exceeding these may cause upload errors.
- Mobile app available for convenience.
Practical Use Cases
- Personal “everything notebook” for collecting diverse notes, quotes, videos, and articles.
- Thematic notebooks for deep dives into specific topics (e.g., education, psychology, health).
- Building collective knowledge bases for teams (e.g., HR processes, training materials).
- Summarizing, extracting insights, and organizing large datasets (e.g., survey feedback, meeting transcripts).
- Content creation (e-learning, quizzes, podcasts) based strictly on verified resources.
Comparison with Other Tools
- Unlike ChatGPT or Gemini, which draw on global internet data, Notebook LM relies solely on user-uploaded data.
- More comprehensive and reliable for in-depth work with user-owned material due to persistent document mapping.
Responsible Use & Limitations
- Always verify sourced information as AI can misinterpret or amplify existing biases in documents.
- Avoid over-reliance—reading and understanding original materials remains crucial.
- Use AI as a support tool, not a replacement for learning and critical thinking.
Tips for Effective Practice
- Upload high-quality, trustworthy documents for best results.
- Ask broad and specific questions to fully explore and exploit document content.
- Use follow-up questions and suggested prompts to dig deeper, but balance with active, manual inquiry.
- Combine Notebook LM with other tools for tasks like writing or deep research.
Key Terms & Definitions
- Notebook LM — An AI-powered research tool focused on user-uploaded documents.
- Grounded AI — AI that operates only on a fixed, user-defined database.
- Pinning notes — Saving important AI-generated responses for later reference and annotation.
- Mind Map — Visual representation of the relationships between ideas in uploaded content.
Action Items / Next Steps
- Practice creating a new Notebook LM project and upload diverse source types.
- Explore key features: chat, mind maps, audio discussions, and note pinning.
- Review shared example notebooks and experiment with different use cases.
- Stay critical: cross-check AI outputs with original documents for accuracy.