Getting Started with AI: NotebookLM

 If you followed along with a previous post on a holiday challenge for learning AI you may now be wondering where too next? Great question, shows you have learned about prompt engineering and are now thinking there has to be more. There is more, a lot more. A good set of skills and understanding of prompt engineering would serve you very well, and you could stop there for a while. Particularly, if you iterate your prompts and increase your literacy in creating prompts. And remember AI can help you improve your prompting.

For many people, I have found that once the intermediate understanding of prompting is achieved the question doesn't seem to go to how do I prompt better. The question seems to go to, can this be automated or can my LLM be more subject specific or can the LLM be restrained personally to my own knowledge. I want the AI to be more specific or give more weight to a narrower or more personal domain of knowledge.

Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation (RAG) is like giving an AI system a personalized library to reference while it's talking to you. Instead of only relying on what it learned during training, RAG lets AI search through specific documents or data to find relevant information before generating a response. Think of it like a student who first checks their textbook and notes before answering a question, rather than just going off memory. This helps the AI give more accurate and up-to-date answers based on reliable sources. 

There are a few online options to provide you a personal RAG platform. Currently, my two favorites are perplexity.ai and NotebookLM. Both these platforms allow you to upload or reference other resources (text, video, and others) to augment (and focus) your use of AI. Really very amazing at supporting you in creating subject specific AI mentors. I strongly suggest you begin to play with NotebookLM.

Consider using NotebookLM

1. Set up an account (or use it with your existing google account). https://notebooklm.google.com/
2. Watch a NotebookLM introductory overview video: https://youtu.be/UG0DP6nVnrc?si=2bGoT7ZMI-VKsU6_
3. Think about business and personal use cases: https://youtu.be/U3SgtCWsjXg?si=eR_ESarUJTHenPki
4. Consider the history of NotebookLM development at google: https://youtu.be/sOyFpSW1Vls?si=F9gVrxXrc2vihRnf

If you remain curious about where RAG and automated agents fit into all the near future of AI I published a post last week discussing these two innovations with AI. Twenty twenty-five will be an interesting year.