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Expected size of the global Generative AI market by 2030.
Average Salary of an Machine Learning Engineer annually.
In this Build an App with RAG course, you will learn to create advanced AI-powered applications using Retrieval-Augmented Generation (RAG) techniques. This course covers how to integrate data retrieval with generative AI models like GPT to enhance app functionality. You'll gain hands-on experience in building context-aware applications that retrieve relevant information and generate high-quality responses, making it ideal for those looking to develop smarter, more e
Read MoreThis course teaches you how to build an application using Retrieval-Augmented Generation (RAG), a technique that combines information retrieval with generative AI to provide context-aware, accurate outputs. You will learn to integrate APIs, manage external data sources, and use AI models like GPT to generate meaningful responses.
While basic programming knowledge (especially Python) is helpful, the course focuses on no-code/low-code methods using RAG, so it's beginner-friendly for those without extensive coding experience.
RAG is a technique that enhances generative AI models by retrieving relevant information from external data sources before generating a response. It combines the power of search with natural language generation to provide more accurate and contextually rich outputs.
The course covers using GPT-based models, like GPT-3.5 or GPT-4, and other retrieval models for integrating external data and improving response accuracy.
You will learn to use APIs, external data sources, LangChain, and other tools to connect and enhance the functionality of your RAG-powered application.
Yes! By the end of the course, you will have the knowledge to build and deploy your own RAG-powered applications, such as chatbots, question-answering systems, and personalized recommendation engines.