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Understanding LLMs: GenAI, RAG & Prompting Fundamentals

Packt via Coursera

Overview

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Unlock the potential of generative AI and LLMs with practical lessons on image generation, transformers, prompt engineering, and RAG. Learn how to refine prompts for real-world success in AI projects. This course introduces learners to generative AI, large language models (LLMs), and retrieval-augmented generation (RAG). Beginning with foundational AI concepts and practical applications, participants explore how generative AI tools like ChatGPT, Gemini, and Midjourney produce high-quality images and videos. The course then dives into the mechanics of transformers, helping learners understand how LLMs generate and process language. Focusing on practical techniques, students will master prompt engineering, including zero-shot and few-shot prompting, and learn how to evaluate and optimize AI models. Case studies provide real-world insights into refining prompts for different scenarios. The course also covers multimodal models and the transformative power of RAG in enhancing AI's capabilities. By the end, learners will be proficient in using LLMs, designing optimized prompts, and exploring cutting-edge applications of generative AI, making this course essential for professionals looking to leverage AI in real-world projects. This course is designed for technical professionals, AI enthusiasts, and individuals eager to dive into the rapidly evolving world of generative AI and LLMs. It's ideal for software developers, data scientists, machine learning engineers, and anyone interested in AI applications. No prior knowledge of AI is required, but a basic understanding of programming and machine learning concepts will be helpful. Participants should be motivated to learn practical skills for applying generative AI and prompt engineering in real-world scenarios. This course takes you on a comprehensive journey from foundational AI concepts to advanced applications. You'll dive into generative AI technologies, understand how large language models (LLMs) work, and master practical techniques like prompt engineering, all while exploring their real-world applications. This course is based on Understanding LLMs - Generative AI, RAG, and Prompting Fundamentals, by Delta X Technology LLC. Copyright © 2026 Justin Barnett. All rights reserved. No part of this course may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews. Packt is one of the world's most prolific publishers of cutting-edge technical content. For over two decades we've made it our mission to curate and publish the knowledge of only the very best technical experts. We focus on real-world courses that help our customers get the job done, with coverage that extends across a wide range of established and cutting-edge technical topics. If you're an individual or an organisation that embraces learning by doing, Packt is the perfect fit for you.

Syllabus

  • Introduction
    • This module introduces learners to the fundamentals of generative AI, including its importance, real-world applications, and key terminology. It provides an overview of the course structure and highlights essential AI concepts for beginners.
  • Generating Images and Video with AI Using Midjourney, ChatGPT, Gemini, and More
    • This module explores the use of AI tools like ChatGPT and GANs for generating images and videos, while also examining real-world applications through case studies. Learners will gain an understanding of the technical and creative potential of AI in visual content creation.
  • What Are Transformers and How Do LLMs Work
    • This module provides an in-depth exploration of how Large Language Models (LLMs) operate, covering transformer architecture, prompt engineering techniques, and methods for evaluating AI performance. Learners will gain a comprehensive understanding of generative AI systems and how to effectively interact with them.

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