#38 Back to Basics — RAG, Transformers, ML Optimization, and LLM Evaluation.

The post delves into the relevance of RAG (Retrieval-Augmented Generation), comparing it against models like Gemini that process millions of tokens. It highlights why RAG will remain useful for specific applications. There's a mention of a free masterclass on AI tools, a project spotlight on an AI-driven job search assistant, and various collaboration opportunities in the AI community. A featurette on a Streamlit app for RAG evaluation and discussions on the importance of transformer architecture in NLP and querying SQL databases using LLM agents are also included.