Introduction to RAG

Overview

What you'll learn

• LLMs and prompt engineering
• embeddings and vector databases
• information retrieval techniques
• RAG pipeline development
• data preprocessing and document handling
• and managing challenges like hallucinations
• latency
• and scalability

Syllabus 6

1. Foundation of RAG

   In this module, you will learn about introduction to RAG along with the different types of RAG, challenges associated with RAG and some real-world applications.

2. Core Concepts of RAG Systems

   In this module, you will learn about the vector stores, FAISS and Pinecone, Embeddings , Retrieval Mechanisms, Latency and Scalability in Production of RAG Systems are also discussed.

3. Types of Retrieval-Augmented Generation Systems

   In this module, different types of RAG like Naive RAG, Advanced RAG and Modular RAG are discussed in detail

4. Building a Zero-Cost RAG Engine

   This module helps in building a Zero-Cost Retrieval-Augmented Generation (RAG) Engine using freely available tools and libraries, including Hugging Face, FAISS, Sentence-Transformers, and PyMuPDF.

5. Fixing Hallucinations & Hybrid Search

   This module helps in understanding fixing hallucinations in RAG systems and applying hybrid search for more reliable retrieval.

6. Mini Project

   This module helps in Building a RAG‑Based PDF Question Answering & Video Retrieval System to overcome the inherent limitations of large language models (LLMs)

Advantages and disadvantages

Some points apply to every course of this kind; see how we rank.

Taught by

• Chaitra BChaitra B. is a dedicated content strategist at Great Learning with over seven years of experience in teaching and the…