NotebookLM is the most tailored AI tool I’ve used for knowledge workers. It truly helps me structure massive information and dramatically boosts my learning and content creation efficiency.
As a lifelong learner who reads technical specs and researches open-source projects, I’ve always sought a tool that can “shortcut” my way through mountains of material, reduce mechanical reading, and help me quickly build a global understanding. NotebookLM has been the smoothest and most reliable experience for me over the past year.
It’s not a traditional “chat-style AI tool”—it’s more like an AI-native learning and content organization system that ingests your materials, organizes them, and presents them in various structured formats. The more I use it, the more I realize its help in learning new technologies, understanding unfamiliar fields, organizing large project documents, and building teaching materials—things that general large language models (LLM, Large Language Model) simply can’t match.
The Core Value NotebookLM Brings Me
NotebookLM has significantly improved my workflow, especially in learning new technologies, organizing documents, and content creation.
Quickly Understanding New Technologies: Feed in Complex Materials, Get a “Learnable Version”
My most frequent and indispensable scenario is learning a completely unfamiliar technology or development framework. Faced with dozens or even hundreds of pages of documentation, my typical approach is:
- Add official docs, README files, design documents, and architecture diagrams into a single Notebook
- Let NotebookLM generate:
- Study guides
- Briefings
- Key knowledge points
- FAQs
- Quizzes
- Ultimately, I get a clearly structured “learning entry point” instead of a flood of raw materials.
The following flowchart illustrates how NotebookLM compresses complex documents into a learnable structure:
In the end, what I gain is an “organized knowledge system” rather than a pile of PDFs waiting to be consumed.
Generating MindMaps: Instantly Turning Large Documents into Structured Knowledge Graphs
I rely heavily on MindMaps to build the “skeleton of knowledge.” NotebookLM’s MindMap feature stands out for:
- Automatically identifying relationships between topics
- Interactive node expansion and collapse
- Integrating multiple source documents
Although it currently only exports PNG, the logical structure itself is already an excellent “knowledge compression.”
The table below compares the auto-generation and visualization capabilities of different tools:
| Tool | Auto-Generation | Multi-Doc Integration | Visualization Quality | Export Formats |
|---|---|---|---|---|
| NotebookLM | Strong | Strong | Good | PNG only (SVG not yet supported) |
| Common LLM Tools | Weak | Weak | Poor | Depends on tool |
| MindMap Software (Manual) | None | None | Strong | Fully supported |
NotebookLM’s greatest advantage is automation.
Generating Teaching Outlines, Training Scripts, and Book Structures: Truly Saving Me Time
NotebookLM is more than just “summarization”—it can generate formal teaching structures based on my prompts. By feeding in project docs, API references, architecture designs, case studies, videos, and blogs, and prompting it to generate:
- Teaching outlines
- Project training manuals
- Course structures
- Book chapter frameworks
- Slide text
- Training case descriptions
For anyone who needs to create content, conduct training, or give presentations, this feature is a huge time-saver.
Below is a typical prompt I actually use:
Based on the provided content excerpts, write a detailed training manual that systematically explains the core principles covered. The manual should use a professional and instructional tone, breaking down complex concepts into actionable steps and lessons. Ensure all content is strictly based on the source material and covers every aspect mentioned.
The training manual should include:
1. Training objectives and expected outcomes
2. Training content and structure
3. Training methods and tools
4. Training evaluation and feedback
5. Training summary and follow-up actions
6. Training cases and examples
7. Training resources and references
The results are often surprisingly good.
Multi-Format Input Capability: The Most Stable I’ve Seen
NotebookLM supports direct ingestion of various material types, with extremely stable parsing. The table below summarizes my actual experience:
| Input Type | My Actual Experience |
|---|---|
| Most stable, clear structure parsing | |
| Google Docs | Syncs instantly, very smooth |
| Word / PPT | Recognized normally |
| YouTube Video | Auto-summary + key content extraction, very useful |
| Website URL | Depends on site structure, high success rate |
| Plain Text | No issues |
| Images | Partial success, sufficient for screenshots |
By contrast, other tools often have format parsing issues, garbled text, missing content, or skipped paragraphs. NotebookLM is especially stable in “multi-format ingestion.”
My Most Common NotebookLM Workflow
The following flowchart shows my daily workflow with NotebookLM:
Essentially: let AI help me grasp the big picture → then dive deeper → then output content.
My Suggestions and Minor Regrets
NotebookLM is already excellent, but I still have some strong expectations for future improvements:
MindMap Export Formats Should Support SVG or Text-Based (Markmap)
Currently, only PNG is supported, which gets blurry when enlarged. The table below lists my expectations for future features:
| Expected Feature | Purpose |
|---|---|
| SVG Export | For writing books, making slides, scalable without loss |
| Markmap Output | Most friendly for Markdown writers |
| Raw JSON | Allows custom rendering |
I’m especially looking forward to NotebookLM supporting Markmap format export, which would be extremely friendly for users who write blogs and docs in Markdown.
Recently, Google also launched CodeWiki , similar to DeepWiki , which auto-generates image-rich Wikis for GitHub projects, but currently does not support Mermaid or Markmap.
Conversation History Should Support Long-Term Saving
Currently:
- Chats are not persistently saved
- Only manually “add to notes” preserves results
This causes some knowledge context to be lost. I hope to see a “Notebook conversation history” feature in the future.
Slide Generation Should Support Templates for Content Creators
Currently, Video Overview offers various visual styles, but cannot:
- Upload custom PPT templates
- Apply enterprise/personal branding templates
If PPT template support is added, NotebookLM could become the “video generation hub” for content creators.
Deep Research Should Launch Soon and Be Fully Open
I’m especially looking forward to this feature, as it could upgrade NotebookLM from a “knowledge organization tool” to a “research-grade tool.” I hope it will:
- Reliably crawl more public web pages
- Ensure citation quality
- Integrate with existing Notebook materials
This is a major upgrade I personally care about.
Mobile Experience Should Be Enhanced Beyond Content Playback
Currently, the mobile experience is minimal, only allowing:
- Listening to audio
- Viewing Notebook Guide summaries
- Simple Q&A
I hope mobile will soon support:
- Editing Notebooks
- Deep conversations
- MindMap interaction
- Content output (generating docs, outlines, etc.)
Summary
NotebookLM is truly one of the AI tools I use every single day because it achieves a critical goal:
Organizing information, structuring knowledge, so I don’t have to start from scratch with massive documents.
Whether it’s:
- Learning new technologies
- Reading long documents
- Creating courses
- Conducting training
- Writing books
- Drafting speeches
- Summarizing content
It saves me a huge amount of time upfront, letting me focus on “understanding” and “creating.”
I’ll continue to use NotebookLM as one of my essential tools and keep an eye on its progress in Deep Research, template systems, and mobile features.
This is a tool truly designed for “knowledge workers” and deserves to be known by more people.