AI Engineering & RAG - The document discusses building a unified query engine over data spread across several sources using vector DB and RAG (Retrieval-Augmented Generation) [1] - It presents a scenario of an AI engineer interview at Google, focusing on querying data from sources like Gmail and Drive [1] - The author shares tutorials and insights on DS (Data Science), ML (Machine Learning), LLMs (Large Language Models), and RAGs [1]

AI Infrastructure Tools - Tensorlake enables transformation of unstructured documents into AI-ready data [1] - Zep facilitates building human-like memory for Agents [1] - Firecrawl empowers LLM applications with clean web data [1] - Milvus provides a high-performance vector DB for scalable vector search [1]