A novel use for Google's NotebookLM is to act as an impartial judge. By uploading audio recordings of multiple pitches and providing specific judging criteria (e.g., innovation, impact, storytelling), the AI can analyze the content and announce the winners, adding a unique and data-driven element to events.
Tools like Notebook LM don't just create visuals from a prompt. They analyze a provided corpus of content (videos, text) and synthesize that specific information into custom infographics or slide decks, ensuring deep contextual relevance to your source material.
New features in Google's Notebook LM, like generating quizzes and open-ended questions from user notes, represent a significant evolution for AI in education. Instead of just providing answers, the tool is designed to teach the problem-solving process itself. This fosters deeper understanding, a critical capability that many educational institutions are overlooking.
Founders can use AI pitch deck analyzers as a "sparring partner" to receive objective feedback and iteratively improve their narrative. This allows them to identify weaknesses and strengthen their pitch without burning valuable relationships with real VCs on a premature version.
While many use Google's NotebookLM for summarizing sources, its ability to generate visually appealing and well-structured slide decks is a powerful, overlooked feature. By inputting a source like a transcript or blog post, users can create high-quality presentations, making it a valuable AI slide designer beyond just research.
Individual sellers can use free tools like Google's NotebookLM to build their own specialized AI agents now. By uploading books, articles, and podcasts on topics like prospecting or upselling, they create a personal knowledge base to get instant, tailored answers and stay ahead of the curve.
To create a reliable AI persona, use a two-step process. First, use a constrained tool like Google's NotebookLM, which only uses provided source documents, to distill research into a core prompt. Then, use that fact-based prompt in a general-purpose LLM like ChatGPT to build the final interactive persona.
To make company strategy more accessible, Zapier used Google's NotebookLM to create a central AI 'companion.' It ingests all strategy docs, meeting transcripts, and plans, allowing any employee to ask questions and understand how their work connects to the bigger picture.
A powerful learning hack: 1) Ask an LLM (like Gemini) for a deep research guide on a topic. 2) Paste the text into Google's NotebookLM. 3) Prompt NotebookLM to "create a five-minute podcast" summarizing the material. This transforms dense information into a quick, digestible audio primer for learning on the go.
To codify a specific person's "taste" in writing, the team fed the DSPy framework a dataset of tweets with thumbs up/down ratings and explanations. DSPy then optimized a prompt that created an AI "judge" capable of evaluating new content with 76.5% accuracy against that person's preferences.
Asking an AI to 'predict' or 'evaluate' for a large sample size (e.g., 100,000 users) fundamentally changes its function. The AI automatically switches from generating generic creative options to providing a statistical simulation. This forces it to go deeper in its research and thinking, yielding more accurate and effective outputs.