NotebookLM: I Built a "Prompt Engineer" (Free & Unlimited)

How to Build an Efficient AI Tool Workflow

Introduction to the Problem

• The speaker highlights the rapid obsolescence of tools in the tech landscape, emphasizing that merely memorizing tool names is ineffective.

• Instead of providing a simple solution, the speaker aims to create a comprehensive system for selecting tools and generating prompts automatically.

Setting Up the System

• The process begins with Google Notebook LM, where trusted sources are selected to prevent AI hallucinations.

• Four key sources are identified:

• Rundown AI for daily news and tutorials.

• The Neuron for creator-friendly content focused on impact.

• Futuredia.io as a directory of numerous tools.

• Product Hunt for trending tools.

Configuring AI Behavior

• To enhance AI performance, custom instructions are set by assigning it the role of "head of innovation" with access to industry newsletters.

• A specific scenario is tested where the AI recommends a tech stack for a low-budget sci-fi short film based on provided constraints.

Analyzing Results

• The AI effectively cross-references its database, suggesting specific tools while explaining their affordability and ease of use.

• Limitations are noted; Notebook LM cannot browse the web and requires exact URLs for data input. Users must manually update links weekly.

Writing Scripts with AI

• The next challenge involves using AI to write scripts that don't sound robotic. A personal ghostwriter strategy is introduced by analyzing successful YouTube channels.

• Popular videos from respected channels are used as reference material by copying their URLs into Notebook LM.

Refining Script Generation

• Custom chat configurations change the goal to replicate tone and style rather than summarize content. This transforms the AI from an assistant into a writer.

• A master prompt is created requiring only video length and outline input, resulting in outputs that mimic viral video styles accurately.

Overcoming Technical Hurdles

• The final hurdle discussed is technical execution—getting image generators to produce desired results through proper prompt usage.

• Users are advised to find official documentation or high-quality community guides for premium tools like Nano Banana Pro and V3, ensuring they save these resources as PDFs for easy reference.

How to Effectively Use Technical Documentation for Image Generation

Configuration and Prompt Engineering

• The speaker emphasizes the importance of configuring settings correctly, acting as a senior prompt engineer while referencing uploaded technical documentation.

• A focus is placed on understanding the underlying principles ("how to fish") rather than just obtaining results ("the fish").

Creating Effective Prompts

• The speaker demonstrates creating a prompt for a macro shot of a robotic eye, utilizing documentation to determine optimal aperture settings for background blur.

• The response not only provides the prompt but also includes a technical breakdown, enhancing understanding of the process.

Generating Images with AI Tools

• After generating an image using Nano Banana Pro, the results showcase perfect blur and sharp detail, indicating adherence to previously unknown rules.

• A second example involves generating video content; the prompt for a high-speed FPV drone shot above a waterfall captures dynamic elements effectively due to correct terminology usage.

Insights on AI Optimization

• The AI's ability to predict digital distortion from drone proximity highlights its advanced optimization capabilities, suggesting it understands manual instructions better than developers themselves.