AI Systems Development using LLMs

Large language models have rapidly transformed artificial intelligence, natural language processing, and modern machine learning, making them essential tools for modern software development. This book is your practical roadmap to understanding these technologies and building intelligent, real-world AI applications.

This book systematically covers the essential concepts needed to build, scale, and secure intelligent systems. It begins with the foundations of large language models, scaling laws, ethical considerations, and developer tools like LangChain, LlamaIndex, and Ollama. Next, it guides you through prompt engineering, reasoning techniques, and semantic vector search using custom embeddings. You will then learn how to design advanced retrieval-augmented generation (RAG) systems with vector databases like ChromaDB, FAISS, and Weaviate, alongside conversational memory and evaluation frameworks. Finally, the book covers autonomous AI agent orchestration, multimodal models such as GPT-4o and LLaVA, cloud-to-edge deployment, optimization techniques like LoRA and quantization, prompt security, and emerging standards such as the model context protocol.

By the end of this book, readers will be well-equipped to design, deploy, and secure production-grade AI systems, possessing the practical skills required to solve complex real-world engineering challenges. They will learn to build scalable, secure, and trustworthy LLM applications for academic, industrial, and entrepreneurial settings.

WHAT YOU WILL LEARN
● Master LLM foundations, scaling, and LangChain ecosystem framework tools.
● Orchestrate multi-agent systems with ReAct workflows and safety interlocks.
● Build conversational and RAG applications using vector databases and evaluation frameworks.
● Develop, orchestrate, govern, and secure task-oriented AI agents.
● Deploy and optimize scalable, secure, and multimodal LLM applications.
● Implement semantic chunking across ChromaDB, FAISS, and Weaviate databases.

WHO THIS BOOK IS FOR
This book is intended for undergraduate and postgraduate students, AI/ML engineers, data scientists, software developers, researchers, system architects, cloud engineers, educators, startup teams, and professionals seeking to build practical applications using large language models. Basic familiarity with Python programming and introductory machine learning concepts is recommended.

1. Introduction to Large Language Models
2. LLM Tools and Frameworks
3. Open-source LLM Ecosystem
4. Ethics, Bias, and Responsible AI
5. Fundamentals of Prompt Engineering
6. Advanced Prompt Engineering Techniques
7. Embeddings and Semantic Search
8. Fine-tuning and Customizing Embeddings
9. Chunking Strategies and Vector Databases
10. Conversational Applications with LLMs
11. Designing Retrieval-augmented Generation Systems
12. Advanced RAG and Explainability
13. Building Foundations of AI Agents
14. Orchestrating and Securing AI Agents
15. Deployment and Optimization of LLM Systems
16. Security and Safety in LLM Applications
17. Multimodal LLMs for Text, Vision, and Speech
18. Future of AI Systems with LLMs

Dr. Muralidhar Kurni has more than 25 years of experience in teaching, research, and academic leadership. He currently serves as an Associate Professor in the Department of Computer Science and Engineering, Dean of Research & Development (R&D), and Dean of Innovation & Entrepreneurship at Anantha Lakshmi Institute of Technology & Sciences (Autonomous), Anantapuramu, India. He holds a Ph.D. in Computer Science and Engineering from JNTUA and has completed postdoctoral research at the University of South Florida, USA. His expertise includes artificial intelligence, machine learning, high-performance computing, and quantum computing. A Senior Member of IEEE and an International Engineering Educator (IGIP), he has authored and edited several books with leading publishers. He has published extensively in reputed SCIE and Scopus-indexed journals. Through his teaching, research, and workshops, he has consistently focused on making complex computing concepts accessible, with a strong emphasis on Python-based learning and practical quantum computing education.

Dr. Srinidhi Hiriyannaiah is an accomplished researcher and industry professional in applied artificial intelligence, with over a decade of experience spanning academic research and industrial innovation. He currently serves as a Staff AI Scientist at GE Healthcare India, where he contributes to the designdesigning and deployment ofdeploying AI solutions. He holds a Ph.D. in Computer and Information Sciences and specializes in machine learning, deep learning, natural language processing, and multimodal data analytics. His work focuses on developing scalable and interpretable AI solutions for clinical data summarization, patient journey analysis, and decision-support systems. Dr. Srinidhi has published extensively in peer-reviewed international journals and conferences, contributing to advancements in healthcare AI and computational intelligence. His research integrates domain expertise with data-driven methodologies, emphasizing responsible AI practices and real-world applicability in complex healthcare environments.

Dr. Srinivasa K. G. has over two decades of experience in teaching, research, and academic leadership. He currently serves as Professor of Data Science and Artificial Intelligence and Dean (Academics) at DSPM IIIT-Naya Raipur, India. He holds a Ph.D. in Computer Science from Bangalore University and has served at several reputed institutions, including NITTTR Chandigarh, MSRIT Bangalore, and CBP Government Engineering College, New Delhi. A Senior Member of IEEE and ACM, he has authored numerous books and published more than 150 research papers in international journals and conferences. His expertise includes artificial intelligence, high-performance computing, and emerging quantum technologies. He has received several prestigious awards, including the AICTE Career Award, ISTE Best Research Award, IEI Young Engineer Award, and BOYSCAST Fellowship from the Department of Science and Technology, Government of India. He actively contributes to advancing research and education in next-generation computational systems, including quantum computing.