Advanced Retrieval-Augmented Generation (RAG) for Large Language Models
Scale your tech career with cutting-edge AI applications to build advanced RAG systems and optimise LLMs with Pragmatic AI Labs.
Duration
5 weeks
Weekly study
2 hours
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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.
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.
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.
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.
In the first video we present the overview of the course, and the main challenges of successfully implementing AI applications in enterprise companies.
Instructions to setup Jupyter notebook environment to execute the hands-on labs and exercises notebooks.
Introduction video to simple RAG applications
Video walkthrough of the hands-on lab building a simple RAG application.
Discussion on the recall-precision trade-off and its impact on the performance of RAG applications.
Review of the key points of the week, homework exercise on simple RAG notebook, and end-week quiz.
Introduction to text embedding
Walkthrough of the hands-on lab on the various aspects of embedding including tokenization, vocabulary and embedding visualization.
Lesson Reflection on the key terms and points covered in the lesson, and week-end quiz.
Video presentation of the need for document splitting and chunking and the main methods used to perform it.
Video walkthrough of the hands-on on Semantic Chunking, one of the popular chunking methods.
Deeper overview of chunking strategies
Hands-on lab to chunking enrichment using document context and LLM models.
Discussion of the issue of misalignment of users' queries and document chunks.
Hands-on lab on a powerful method to generate hypothetical questions for document chunks to improve query-document alignment.
Summary and review of the main key terms and points discussed this week.
Advanced exercise notebook to practice various topics and methods of document chunking.
Quiz to test the understanding of the main topics of document chunking.
Introduction to Hybrid Search in RAG application to improve document recall in the retrieval step.
Hands-on lab to implement Hybrid Search in RAG application using sparse index with BM25.
Hands-on lab to implement reranking of Hybrid-Search results in RAG application using a cross-encoder.
Summary of key terms and points from the Hybrid Search lessons.
Homework exercise to implement a more sophisticated Hybrid Search using SPLADE and Cohere reranking models.
Quiz on the hybrid search week topics.
Video introduction presentation of building RAG application for image based documents using Multimodal techniques.
Hands-on lab of building a multimodal RAG application on image based documents.
Overview of the main key points and terms covered in the Multimodal week.
Test on the main topics covered in the Multimodal week lesson.
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