Master Retrieval-Augmented Generation (RAG)
This learning path is designed specifically for machine learning engineers, AI developers, and data engineers seeking to enhance their LLM applications with retrieval-augmented generation (RAG). After completing this learning path, learners will be equipped to design, implement, and optimize RAG solutions that improve accuracy and efficiency in AI applications while reducing operational costs. Understand the fundamentals of RAG and fine-tuning. Learn practical vector database implementations. Understand sophisticated RAG applications. Build hands-on projects using Azure AI and LlamaIndex.
Courses
- RAG and Fine-Tuning Explained
- Vector Databases in Practice: Deep Dive
- Generative AI: Introduction to Large Language Models
- LLM Foundations: Vector Databases for Caching and Retrieval Augmented Generation (RAG)
- Advanced RAG Applications with Vector Databases
- Building RAG Solutions with Azure AI Foundry (Formerly Azure AI Studio)
- Hands-On AI: RAG using LlamaIndex