Introduction to Generative AI with Large Language Models

Build Intelligent Applications Using Prompt Engineering, RAG, and Multimodal Tools

Author

Moody Amakobe

Published

January 1, 2025

Welcome

Welcome to Introduction to Generative AI with Large Language Models!
This Open Educational Resource (OER) is designed for graduate students and practitioners who want to build real, production-minded GenAI applications, not just learn the theory.

Across the chapters, you will progressively build intelligent systems using: - Prompt engineering and evaluation - Retrieval-Augmented Generation (RAG) pipelines - API integration patterns and DevOps practices - Multimodal tools and workflows - Safety, ethics, and responsible deployment

Abstract

Large Language Models (LLMs) have reshaped how software teams build interfaces, automate knowledge work, and deliver user-facing intelligence.
This book provides a practical, engineering-first pathway from fundamentals to production deployment, with hands-on milestones in every chapter.

Learning Objectives

By working through this book, you will be able to: - Explain LLM concepts (tokens, embeddings, attention, transformers) and how they impact application design. - Design prompts, templates, and evaluation workflows for reliable, controllable outputs. - Build RAG systems with chunking, embeddings, vector search, and grounded generation with citations. - Integrate multiple model providers with retries, caching, observability, and cost controls. - Implement multimodal pipelines for images, documents, and audio. - Apply responsible AI practices (privacy, bias mitigation, safety controls, red-teaming). - Deploy and operate GenAI systems with CI/CD and production monitoring.

License

This book is published by Global Data Science Institute (GDSI) as an Open Educational Resource (OER) under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

CC BY 4.0

How to Use This Book

  • Prefer the HTML edition for interactive content and embedded demos.
  • Use PDF for offline reading and sharing.
  • Draft chapter sources are included in assets/drafts/.