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I Fed My Tech Book into NotebookLM, and It Generated Spot-On Infographics

Recently, I published a technical book (in Japanese) titled "The YouTube Timer Quest: Mastering the Engineer's Mindset via Chrome Extensions."

Out of curiosity, I decided to feed my entire Japanese book into Google's NotebookLM to see how it would summarize the content. The result? It generated surprisingly accurate, professional-grade infographics—in natural English!

Even if you haven't read my book, I want to share two of these diagrams and explain why this blew my mind as a developer and author.

How AI Visualized the Author's Intent

First, take a look at the initial infographic it generated.

NotebookLM Generated Infographic 1

As the author, I was astonished by how well it captured both the core philosophy and the specific technical challenges discussed in the book.

My book uses the development of a real-world Chrome extension (Watchtime Badge) to teach readers how to move beyond simple "copy-paste" coding and adopt an actual "Engineer's Mindset." It focuses heavily on solving real-world browser constraints.

Here is why this diagram is so impressive:

  • Extracting the Core Philosophy: The diagram clearly illustrates the "Define, Decompose, Build" pillars. This isn't just a specific chapter; it's the foundational theme of the entire book. The AI understood the underlying message remarkably well.
  • Pinpointing Technical Solutions: In the middle right, you'll see "Delta Time for Precision". This directly corresponds to my chapter explaining why JavaScript's setInterval drifts over time and how to fix it using timestamp differences.
  • Understanding API Context: It highlighted the use of the chrome.alarms API as "The Heartbeat for Survival". This reflects my chapter on how to revive Chrome Service Workers that are automatically terminated by the browser's energy-saving features.

The Secret: NotebookLM Prompt Settings

To generate these high-quality diagrams, I used the following settings in NotebookLM:

  • Choose Language: English
  • Choose Orientation: Landscape
  • Level of Detail: Standard

đź’ˇ Author's Tip: Even though my source material was completely in Japanese, I intentionally set the output language to English. Why? Because for technical diagrams, English text tends to fit the layout much better visually, resulting in a cleaner, more standard, and stylish look. NotebookLM handled the cross-lingual summarization seamlessly.

Here is the second infographic it generated:

NotebookLM Generated Infographic 2

While the first infographic visualizes the development process as a dynamic journey (a "quest"), this second one organizes the same knowledge into a structured, static overview.

It clearly categorizes the abstract "Engineer Thinking" on the left and concrete "System Constraints" on the right. It is fascinating to see how the exact same prompt generated two completely different visual approaches—process versus structure.

Conclusion: What Engineers Should Focus On

Seeing these images made me realize something profound: We no longer need to spend hours manually formatting our ideas into visual diagrams.

AI can now effectively grasp technical context, understand system constraints, and translate complex concepts into beautiful visuals. This frees us engineers to let go of tedious layout adjustments and focus on what truly matters: defining the right problems and designing the core system.

Have you tried generating diagrams with NotebookLM for your projects? Let me know your thoughts in the comments!

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