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Learn Retrieval Augmented Generation online: university courses, full YouTube courses, and courses with free certificates, from the IITs, MIT, Harvard, Google, Microsoft and more.
DeepLearning.AI
LangChain: Chat With Your Data delves into two main topics: (1) Retrieval Augmented Generation (RAG), a common LLM application that retrieves contextual documents from an external dataset, and (2) a guide to building a chatbot that responds to queries based on the content of your documents, rather…
DeepLearning.AI
Gain fundamental understanding and the practical knowledge to develop production-ready RAG applications, from architecture to deployment and evaluation.
Microsoft
Implement advanced retrieval-augmented generation (RAG) pipelines for production AI agents using Azure AI Search and Microsoft Foundry. Design hybrid search architectures, implement reranking strategies, configure dynamic knowledge source routing, and optimize chunking and embedding strategies.
Microsoft
Learn how Foundry IQ transforms how AI agents access and reason over organizational data through its unified knowledge layer and Retrieval Augmented Generation (RAG) capabilities. Build a practical agent that leverages enterprise knowledge through Foundry IQ's agentic retrieval engine.
DeepLearning.AI
Retrieval Augmented Generation (RAG) improves large language model (LLM) responses by retrieving relevant data from knowledge bases—often private, recent, or domain-specific—and using it to generate more accurate, grounded answers. In this course, you’ll learn how to build RAG systems that connect…
Microsoft
Learn how retrieval-augmented generation (RAG) grounds generative AI responses in relevant information.
Microsoft
Implement Retrieval Augmented Generation (RAG) with Azure Databricks
Microsoft
Learn how to implement Retrieval Augmented Generation (RAG) patterns using Azure SQL Database. Identify RAG use cases, prepare database context for Large Language Model (LLM) processing, construct augmented prompts, and process model responses.
Vanderbilt University
Unlock the full potential of generative AI and become a master of prompt engineering. Dive deeper into how you can use In-context Learning to build better and more reliable prompts. See how Retrieval Augmented Generation (RAG) works and what can go wrong that you can counteract with fact-checkable…
Microsoft
Explore complementary strategies to optimize generative AI model performance, including prompt engineering, system messages, model parameters, Retrieval Augmented Generation (RAG), and fine-tuning. Learn when to use each strategy and how to combine them.
Great Learning
In this GLA Free course you will be able to understand the basics and working of Retrieval-Augmented Generation (RAG), along with its core concepts, types, and real-world applications. It also helps in understanding how vector stores, embeddings, and retrieval mechanisms work, along with challenges…
Microsoft
Learn to design intelligent search, work with models and embeddings, and build Retrieval Augmented Generation (RAG) patterns in T-SQL.
IBM
Build in-demand skills in generative AI application development and get hands-on experience creating intelligent chatbots and apps using popular LLMs, RAG technology, and IBM watsonx.
Exploring how AI can generate text, images, and sounds: retrieval-augmented generation, autoencoders, diffusion models. Registration (and assignments) for this course won't be available on edX until later this year, so watching now offers a preview. Unlike CS50 AI, which assume a programming backgr…
IBM
A short hands-on project: Retrieval-Augmented Generation (RAG), Llama, Python.
IBM
A short hands-on project: Retrieval-Augmented Generation (RAG), LLM, LangChain.
Learn how to implement RAG (Retrieval Augmented Generation) from scratch, straight from a LangChain software engineer. This Python course teaches you how to use RAG to combine your own custom data with the power of Large Language Models (LLMs). 💻 Code: https://github.com/langchain-ai/rag-from-scrat…
IBM
Get valuable practical experience building a real-world gen AI application that you can talk about in interviews.
IBM
A short hands-on project: Faiss, Retrieval-Augmented Generation (RAG), Sentence Embeddings.
IBM
A short hands-on project: Retrieval-Augmented Generation (RAG), Reinforcement Learning, Proximal Policy Optimization (PPO).
