If you’re using NotebookLM and want to automate your entire process—from sourcing questions to generating and storing video overviews—you’re not alone. Many educators and content creators feel stuck with manual steps that eat up time. Fortunately, with a strategic setup of local LLMs, browser scripting, and Robotic Process Automation (RPA), you can streamline this workflow for faster, more efficient content creation.
Why Automating This Workflow Matters
This process impacts your productivity and consistency. Manual tasks like copying prompts, searching sources, and downloading videos are time-consuming. Automating frees you to focus on quality, analysis, or expanding your content. Plus, local AI and automation tools offer privacy benefits and reduced dependency on external APIs.
Breaking Down the Automation Approach
To automate NotebookLM workflows locally, you need three main elements:
- Local Language Models (LLMs): Use open-source models like GPT-J or GPT-NeoX for prompt processing. You can run these on your own hardware or cloud.
- Browser Scripting: Automate source discovery, data input, and navigation within NotebookLM using tools like Puppeteer or Selenium.
- Robotic Process Automation (RPA): Use RPA tools like UiPath, Power Automate, or Python scripts to handle file downloads, uploads, and prompt automation.
Step-by-Step Strategy to Automate
- Set Up Local LLMs: Install and configure your chosen open-source LLM. Use it to generate source lists or enhance prompts automatically.
- Automate Source Discovery: Write a script to open NotebookLM, run the Discover Sources feature, and scrape the links for relevant sources. Save these automatically.
- Streamline Source Import & Prompt Creation: Use scripting or RPA to import sources and generate prompts with your predefined prefix and context.
- Trigger Video Generation: Automate the clicking of the “Create customized video overview” button and input the prompt programmatically.
- Download & Save Videos: Monitor for completion, then use scripts or RPA to automatically download videos and upload them to your cloud storage seamlessly.
Quick Tips for Implementation
- Test each step individually before combining, to ensure reliability.
- Use headless browser scripting to run without manual interference.
- Leverage local AI models for sensitive or custom prompt adjustments to reduce API costs and latency.
- Employ simple Python scripts for file handling and API interactions to keep things lightweight and easy to troubleshoot.
- Set up scheduled runs or triggers to automate the entire workflow seamlessly.
What’s Next?
By integrating local LLMs, scripting, and RPA, you can create a near-fully autonomous notebook-to-video process. This approach not only saves time but also makes your workflows more scalable and error-proof. Experiment with small automation modules first, then connect them into a full pipeline for maximum efficiency.
Remember: The key is to start simple, test often, and keep refining your scripts. Automation isn’t a one-time setup—it’s an ongoing process of improvement.