Advanced Retrieval-Augmented Generation (RAG) for Large Language Models

Learn to build RAG systems from scratch and lead LLMs with advanced solutions

Give yourself a leg up in the competitive world of tech by upskilling in one of AI’s most applied techniques: Retrieval-Augmented Generation (RAG).

On this five-week course from Pragmatic AI Labs, you’ll dive into the essential concepts and hands-on skills needed to master RAG and apply them in AI engineering for real-world solutions.

Apply advanced embedding strategies and chunking solutions

You’ll begin this course by exploring the basics of RAG, navigating its core processes and identifying its common pitfalls. With this foundation, you’ll soon move on to learning more advanced techniques, like document embedding and chunking.

Working through hands-on labs, you’ll apply these techniques to real-world data, experimenting with embedding models and refining your chunking strategies to improve document retrieval and alignment.

Create hybrid search systems

Next, you’ll dive into hybrid search in RAG, where you’ll learn how to combine semantic and keyword search to enhance retrieval performance.

You’ll work with both sparse indexing and dense encoding, mastering techniques like BM25 and Sentence Transformers to refine search results.

Plus, you’ll explore reranking strategies, using cross-encoders to improve the alignment of documents with user queries and boost retrieval accuracy.

Design multimodal retrieval applications

By the final week, you’ll explore multimodal RAG for image-based documents. You’ll integrate image encoders, apply quantisation for optimised storage, and build efficient indexing systems.

Through practical exercises and guided labs, you’ll create image embeddings using advanced models, improving search accuracy and optimising vector databases for efficient multimodal systems.

This course is for AI engineers, MLOps professionals, software developers, and data scientists eager to master advanced RAG systems and LLM applications. It’s perfect for those looking to expand their AI skills, integrate cutting-edge technologies into enterprise solutions, or transition into AI development roles.

This course is for AI engineers, MLOps professionals, software developers, and data scientists eager to master advanced RAG systems and LLM applications. It’s perfect for those looking to expand their AI skills, integrate cutting-edge technologies into enterprise solutions, or transition into AI development roles.

• Improve AI applications based on Large Language Models (LLM) using Retrieval-Augmented-Generation (RAG) advanced techniques.
• Assess best use cases for RAG vs traditional GenAI