Scariest part about AI It won't even ask for PTO 🙂

https://www.youtube.com/shorts/uvHeI9f_ZYE

I believe most of you know what RAG is. But some might not. I am personally very exited on the LLM development possibility of Graph RAG. So let me share what in short RAG is as a foundation. Retrieval-Augmented Generation (RAG) is a popular method in AI that solves the problem of language models forgetting or not knowing specific or updated information, especially in real-world or specialized use cases. While traditional in-context learning only uses the few examples or prompts provided at the time of the query, RAG goes a step further by actively searching a connected knowledge base—like a set of documents or a company’s database—for relevant information, then feeding those results into the language model to generate more accurate and grounded answers. This makes RAG especially useful when you need reliable, source-based responses. To implement it, you store your data in a searchable format (usually in a vector database), use an embedding model to match user questions with the most relevant chunks of information, and then give both the question and retrieved content to the language model to generate a smart, well-informed response. See this information: https://www.youtube.com/watch?v=T-D1OfcDW1M&t=18s

China's New Free AI AGENT Shocks The World: 8x Faster Than DeepSeek R1 https://www.youtube.com/watch?v=v2Ur_DgaEEI

Although I've been using Python for data analysis for five years, there are still things I don't remember—especially when it comes to LLM development, which often feels completely new. I find these 4 short courses from deep learning.ai to be one of the best available free courses for beginners in Python or for those looking to reinforce their foundational knowledge: https://learn.deeplearning.ai/courses/ai-python-for-beginners/lesson/z57gn/introduction?courseName=ai-python-for-beginners Yes, tools like Cursor and other IDEs can handle much of the tedious grueling coding works for us now, but understanding what they generate can be really helpful for debugging and improving our development process. Check them out!