Master Retrieval-Augmented Generation (RAG)

Unlock the potential of retrieval-augmented generation (RAG) with this comprehensive learning path. Learn advanced techniques for optimizing text-based RAG through chunking, embedding, and metadata. Master integrating vector databases with large language models and gain hands-on skills in multimodal RAG. Design, implement, and evaluate cutting-edge RAG solutions to stay ahead in AI. Start your journey to elevate your data-driven capabilities today.

Identify key concepts of RAG and vector databases.
Optimize for text-based and multimodal RAG.
Apply new skills in building and deploying RAG solutions.
Evaluate RAG solutions' performance and effectiveness.

Syllabus

Courses under this program:
Course 1: RAG and Fine-Tuning Explained
-This course breaks down the AI concepts Retrieval Augmented Generation (RAG) and fine-tuning to explain how they factor into building robust enterprise applications.

Course 2: Vector Databases in Practice: Deep Dive
-Go beyond the basics of vector databases by building a database and app from scratch, and learn key considerations along the way.

Course 3: Generative AI: Introduction to Large Language Models
-Gain a foundational knowledge of how large language models and other Generative AI models work.

Course 4: LLM Foundations: Vector Databases for Caching and Retrieval Augmented Generation (RAG)
-Learn about the basics of vector databases and how to use them in LLM caching and retrieval-augmented generation.

Course 5: Advanced RAG Applications with Vector Databases
-Discover cutting-edge methods to perform retrieval-augmented generation (RAG) with a vector database.

Course 6: Building RAG Solutions with Azure AI Foundry (Formerly Azure AI Studio)
-Learn to effectively leverage Azure AI Foundry (formerly Azure AI Studio) for the creation of retrieval-augmented generation (RAG) solutions.

Course 7: Hands-On AI: RAG using LlamaIndex
-Learn how to enhance AI query capabilities and data accuracy through the application of LlamaIndex in retrieval-augmented generation processes.