Foundations of Retrieval Augmented Generation (RAG) Systems

Overview

Learn the essentials of Retrieval-Augmented Generation (RAG) to enhance text generation accuracy. This path covers RAG basics, vector databases, semantic retrieval with embeddings, and building a complete RAG pipeline.

Syllabus

Course 1: Introduction to RAG
Course 2: Text Representation Techniques for RAG Systems
Course 3: Scaling up RAG with Vector Databases
Course 4: Beyond Basic RAG: Improving our Pipeline

Courses

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Learn what Retrieval-Augmented Generation (RAG) is, why combining retrieval with generation can reduce hallucinations, and how a basic RAG workflow contrasts with naive prompting. This course is mostly informational, setting the stage for more hands-on work in later courses.
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Learn essential text representation methods for RAG systems, from Bag-of-Words to embeddings. Explore how these techniques enhance understanding and retrieval, visualize embeddings with t-SNE, and compare BOW and embeddings in document retrieval and semantic search.
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Discover how to scale RAG systems using vector databases. Learn to preprocess documents, store embeddings in ChromaDB, retrieve relevant chunks, and construct prompts. Manage updates and large-scale ingestion with batch strategies for efficient retrieval.
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Enhance your RAG pipeline with advanced retrieval techniques. Implement hybrid retrieval, iterative query refinement, and context summarization. Constrain LLM outputs to retrieved context, ensuring accuracy and minimizing hallucinations in final responses.