Query Auto Completion (QAC) aims to help users reach their search intent faster and is a gateway to search for users. Everyday, billions of keystrokes across hundreds of languages are served by Bing Autosuggest in less than 100 ms. The expected suggestions could differ depending on user demography, previous search queries and current trends. In general, the suggestions in the AutoSuggest block are expected to be relevant, personalized, fresh, diverse and need to be guarded against being defective, hateful, adult or offensive in any way. In this tutorial, we will first discuss about various critical components in QAC systems. Further, we will discuss details about traditional machine learning and deep learning architectures proposed for four main components: ranking in QAC, personalization, spell corrections and natural language generation for QAC.