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

About this course

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.<

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