Advanced LLMs with Retrieval Augmented Generation (RAG): Practical Projects for AI Applications
1h 47mAdvanced2025-01-28
Authors
Pragmatic AI Labs
Guy Ernest
Course details
Are you an engineer, solutions architect, or software developer tasked with building enterprise applications? It’s time to get up to speed with the latest AI-powered tools and techniques—in this case, retrieval-augmented generation (RAG). In this course, designed by leading technology educator Pragmatic AI Labs, join instructor Guy Ernest as he outlines the foundational and advanced concepts required to leverage RAG for large language models, including text encoding using embedded vectors, document chunking with enrichment strategies, improving document retrieval, and more. By the end of this course, you’ll be equipped with the skills you need to take advantage of the power of RAG.
Skills covered
Natural Language Processing (NLP)Artificial Intelligence FoundationsArtificial Intelligence (AI)One-Off
Concepts
Introduction
- Course introductions
- Understanding the basics of RAG
- Building a simple RAG example
- Issues with simple RAG
Text Encoding Using Embedding Vectors
- Embedding introduction
- Hands-on lab - Embedding tokenization
- Hands-on lab - Embedding vocabulary
- Hands-on lab - Sentence embedding
- Hands-on lab - Content embedding
- Embedding notebook summary
Document Chunking and Enrichment Strategies
- Chunking introduction
- Hands-on lab - Semantic chunking
- Chunking overview
- Hands-on lab - Contextual retrieval
- Query document alignment
- Hands-on lab - Reverse HyDE
Improving Document Retrieval
- Hybrid search introduction
- Hands-on lab - Hybrid search
- Hands-on lab - Reranking
- Multimodal retrieval introduction
- Hands-on lab - Multimodal PDF retrieval