Google AI 基本功 EP07Gemini 聯手 NotebookLM打造地表最強 AI 知識庫

你會學到

• Google Gemini 深度整合 NotebookLM，實現雙向資料互通與協作，可將 NotebookLM 筆記本內容直接作為 Gemini 對話來源。
• 透過 Gemini 的「筆記本」功能，用戶可集中管理與 NotebookLM 相關的對話歷史，並能自定義提示詞，規範 Gemini 的回應格式與風格。
• Gemini 與 NotebookLM 的整合，讓用戶能根據任務需求，在創意生成（Gemini）與大量檢索（NotebookLM）之間彈性切換，提升工作效率。
• 在 Gemini 中新增的檔案或對話，可同步至 NotebookLM 作為參考來源，反之亦然，形成一個連動的知識庫系統。

教學金句

「你在這邊加入的檔案，也會同步到你 Notebook LM 裡面左側的來源。」 「有點像是專案一樣，你下面所有的對話，他也都會參考當做是你原本的來源。」 「你可以根據這個筆記本去自定義提示詞，有點像是 google GEM 的方式來去回復。」 「等同是 gemini 跟 Notebook LM 就做了一個連動的溝通。」 「當然並不是要取代 Notebook LM，而是讓你更好的在生成還有檢索之間去做自由的切換。」

跟著做

1. 在Gemini左側選單點「筆記本」以啟用。
2. 在筆記本設定中開啟「個人化記憶」並自訂「使用說明」。
3. 可將Gemini對話「移至筆記本」作為來源。
4. 可從Gemini介面快速跳轉至對應的NotebookLM。
5. 在Gemini中可直接新建NotebookLM筆記本。

適用情境

這部影片可以放在 Google AI 系列課程中，作為進階的「AI 知識管理與協作」單元。適合已具備 Google Gemini 和 NotebookLM 基本操作知識，並希望提升 AI 工具整合應用效率的中級學員。學員應已理解兩者單獨的功能，前置知識是了解大型語言模型（LLM）的生成能力與檢索增強生成（RAG）的基本概念。

延伸主題

• 如何利用 Gemini 的自定義提示詞，打造專屬領域的 AI 助手。
• NotebookLM 的進階檢索功能與資料組織策略。
• RAG (Retrieval Augmented Generation) 技術在個人知識庫中的應用實例。
• 跨平台 AI 工具整合的工作流程設計與效益分析。
• 運用 Gemini Pro 的圖片生成能力，結合 NotebookLM 知識庫進行內容創作。

---en---

What You’ll Learn

• Google Gemini deeply integrates with NotebookLM, enabling two-way data interoperability and collaboration, allowing NotebookLM notebook content to be directly used as a source for Gemini conversations.
• Through Gemini’s “Notebook” feature, users can centrally manage conversation history related to NotebookLM and customize prompts to define Gemini’s response format and style.
• The integration of Gemini and NotebookLM allows users to flexibly switch between creative generation (Gemini) and large-scale retrieval (NotebookLM) based on task needs, improving work efficiency.
• New files or conversations added in Gemini can be synchronized to NotebookLM as reference sources, and vice versa, forming an interconnected knowledge base system.

Teaching Quote

“The files you add here will also be synchronized to the sources on the left side of your Notebook LM.” “It’s like a project; all your conversations below it will also be considered as your original sources.” “You can customize prompts based on this notebook, similar to how Google GEM works, to respond.” “This means Gemini and Notebook LM have established an interconnected communication.” “Of course, it’s not to replace Notebook LM, but to allow you to better switch freely between generation and retrieval.”

When to Use

This video can be included in Google AI series courses as an advanced unit on “AI Knowledge Management and Collaboration.” It is suitable for intermediate learners who already have basic operational knowledge of Google Gemini and NotebookLM and wish to improve the efficiency of AI tool integration and application. Learners should understand the individual functions of both, and prerequisite knowledge includes understanding the generative capabilities of Large Language Models (LLMs) and the basic concepts of Retrieval Augmented Generation (RAG).

Related Topics

• How to use Gemini’s custom prompts to create a domain-specific AI assistant.
• Advanced retrieval functions and data organization strategies in NotebookLM.
• Practical examples of RAG (Retrieval Augmented Generation) technology in personal knowledge bases.
• Workflow design and benefit analysis for cross-platform AI tool integration.
• Utilizing Gemini Pro’s image generation capabilities combined with NotebookLM knowledge base for content creation.