[NEW]Mastering Retrieval Augmented Generation (RAG) IN LLMs

Name: [NEW]Mastering Retrieval Augmented Generation (RAG) IN LLMs
Rating: 3.7 (7 reviews)

Quick walkthrough of RAGs

Created byMG Analytics

Last updated 2/2026

English

English [Auto],

What you'll learn

Retrieval Augmented Generation (RAG) IN LLMs
RAG using PDF
RAG Using CSV file
Laoding LLM Models
Ollama
Langchain

Course content

1 section • 7 lectures • 2h 19m total length

Introduction to Gen-AI using LLMs4:19
Master the basics of artificial intelligence, machine learning, deep learning, and transformers, then explore generative AI with large language models and practical use of ChatGPT, Copilot, and Gemini.
Use cases of GEN-AI7:38
Explore how generative ai acts as a thought partner, reasoning tool, and writing assistant for brainstorming, outlining, translating, and summarizing, while avoiding fact retrieval and aiming to improve our LMS.
Introduction to RAG4:14
Explore how retrieval augmented generation acts as a librarian for LLMs, searching external sources and company documents to extract relevant information and inform factual responses.
Huggingface Transformers13:14
Explore huggingface transformers with notebooks and examples for PyTorch and TensorFlow, covering text classification, language modeling, and question answering, and learn fine-tuning on glue with tokenizers and datasets.
Using Langchain to import LLMs53:43
Explore the LangChain ecosystem for building and deploying LLM apps, including chains, agents, retrieval strategies, templates, and tools like Lang Smith and Lang Serve, with Hugging Face and OpenAI integrations.
Using Ollama to extract context from PDFs for LLM34:52
Learn retrieval augmented generation with Ollama, loading PDFs with the unstructured PDF loader, using recursive text splitting, gnomic embeddings, and Chroma DB for multi query retrieval.
Using Ollama to extract context from CSVs for LLM21:31
Learn how to extract context from csvs for llms using retrieval augmented generation, csv loaders, embeddings, and a conversational retrieval chain.

Requirements

Python
Generative AI basics
Interest in GEN-AI

Description

In today's rapidly evolving landscape of artificial intelligence, Large Language Models (LLMs) have emerged as powerful tools for a wide range of applications. However, to truly unlock their full potential, we need to equip them with the ability to access and process external information. That's where Retrieval Augmented Generation (RAG) comes into play.

This course will provide you with a comprehensive understanding of RAG and its applications in enhancing LLM capabilities. You'll learn how to effectively retrieve relevant information from external sources and integrate it into the LLM's responses, making them more informative and accurate.

Course Objectives

Gain a solid understanding of generative AI and LLMs.
Explore the concept of RAG and its benefits.
Learn how to use Langchain to import and interact with LLMs.
Master the process of extracting context from PDFs and CSVs using Ollama.
Apply RAG techniques to enhance LLM performance in various tasks.

Course Structure

Introduction to Gen-AI using LLMs: This introductory lecture will provide a foundational understanding of generative AI and LLMs.
Introduction to RAG: Explore the concept of RAG, its benefits, and how it works.
Using Langchain to Import LLMs: Learn how to effectively import and interact with LLMs using the Langchain library.
Using Ollama to Extract Context from PDFs for LLM: Discover how to extract relevant information from PDFs and incorporate it into LLM responses.
Using Ollama to Extract Context from CSVs for LLM: Learn to extract context from CSV files and integrate it into LLM responses.

Why Choose This Course?

Practical Focus: Gain hands-on experience with RAG techniques and tools.
Expert Guidance: Learn from experienced instructors in the field of generative AI.
Comprehensive Coverage: Explore the entire RAG workflow from importing LLMs to extracting context.
Real-World Applications: Discover how RAG can be applied to various tasks and industries.

Enroll today and unlock the power of RAG to enhance your LLM applications!

Who this course is for:

BEGINNER AI Developers
data Scientists
Analyst
Python Developer