Introduction to Retrieval Augmented Generation (RAG)
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What’s Included
Course Introduction
Course Introduction
In this course introduction video, we'll cover the recommended prerequisites, highlight the benefits of learning about Retrieval-Augmented Generation, provide an overview of key topics, and outline the learning outcomes to help you get the most from this course.
Introduction to RAG
Introduction to RAG
LLMs are static systems, limited to their training data, making them unable to access up-to-date information. In this lesson, you'll learn how RAG enhances AI by dynamically retrieving external data sources, improving accuracy and reliability in AI-generated responses.
RAG Basics
RAG Basics
Building on the introduction, this lesson breaks down the key components of RAG: retrieval, augmentation, and generation. You'll explore how RAG dynamically pulls relevant information, integrates it into AI responses, and provides real-time, fact-based outputs.
RAG and Hallucinations
RAG and Hallucinations
LLMs can generate confident but incorrect responses, known as hallucinations. This lesson examines how RAG reduces hallucinations by grounding AI responses in real-world, verifiable data, improving trust and reliability.
Fine-Tuning vs. RAG
Fine-Tuning vs. RAG
Fine-tuning and RAG both improve AI performance, but they do so in different ways. Here, you'll compare these approaches, understanding the trade-offs in scalability, cost, and domain adaptability to determine when each is the best fit.
RAG and Context Windows
RAG and Context Windows
Context windows define how much information an LLM can process at once. This lesson explores how larger context windows may reduce reliance on RAG, while also explaining why RAG remains crucial for retrieving dynamic, real-time information.
Chunking and Embeddings
Chunking and Embeddings
Retrieving the right information is essential for RAG systems to function effectively. You'll learn how chunking breaks data into manageable pieces and how embeddings convert text into numerical representations for efficient retrieval.
Vectors and Vector Search
Vectors and Vector Search
AI systems rely on vector search to locate relevant information quickly. This lesson explains how vectors store meaning in a multi-dimensional space and how vector search retrieves the most relevant data to inform AI responses.
Observing a RAG Pipeline
Observing a RAG Pipeline
In this course introduction video, we'll cover the recommended prerequisites, highlight the benefits of learning about Retrieval-Augmented Generation, provide an overview of key topics, and outline the learning outcomes to help you get the most from this course.
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