Large Language Models: From Foundations to Production AI Applications — Second Edition

By SK Singh

Large Language Models: From Foundations to Production AI Applications - Second Edition

Learn how Large Language Models work-and how to build, customize, and deploy real-world AI applications from development to production.

Large Language Models (LLMs) such as GPT, Claude, Gemini, Llama, Mistral, and DeepSeek are transforming software development, business automation, search, content generation, knowledge management, and enterprise AI. As organizations adopt Generative AI at scale, there is growing demand for professionals who understand not only how LLMs work, but also how to build, customize, deploy, secure, and operate AI-powered applications.

Updated for the Second Edition, this practical, hands-on guide provides a structured learning path from the foundations of Artificial Intelligence, Natural Language Processing, and Generative AI to Transformer architectures, modern LLMs, Retrieval-Augmented Generation (RAG), fine-tuning, and production AI systems. Rather than focusing only on theory, the book combines technical concepts with architecture diagrams, hands-on labs, code examples, and end-to-end projects that demonstrate how modern LLM applications are designed and built.

What You'll Learn

Foundations of AI, Machine Learning, Deep Learning, NLP, and Generative AI

How LLMs work, including tokenization, embeddings, attention, and Transformer architecture

Encoder, decoder, BERT, GPT, and modern language-model architectures

Major LLM families, including GPT, Claude, Gemini, Llama, Mistral, and DeepSeek

Accessing LLMs through APIs and running local models with Ollama

Prompt engineering, structured outputs, function calling, tool usage, and reliable LLM workflows

Retrieval-Augmented Generation (RAG), embeddings, vector databases, semantic search, hybrid search, and re-ranking

Fine-tuning and efficient model adaptation using LoRA, QLoRA, PEFT, adapter layers, and quantization

Building AI chatbots, enterprise knowledge assistants, PDF applications, and content-generation systems

LLM evaluation, hallucination detection, security, guardrails, safety, and responsible AI practices

Production deployment with REST APIs, FastAPI, Docker, cloud platforms, and serverless architectures

Monitoring, logging, caching, load balancing, cost optimization, GPU inference, vLLM, scalability, and high availability

AI agents and agentic workflows

Hands-On Projects

Apply what you learn by building complete end-to-end AI solutions:

AI Chatbot - Build a conversational AI application using modern LLM APIs, prompt engineering, backend integration, and deployment.

Enterprise Knowledge Assistant - Build a RAG-powered assistant using embeddings, vector search, document retrieval, and organizational knowledge.

Local AI Assistant - Build a private AI assistant using Ollama, local LLMs, and a custom knowledge base that can operate locally.

Source Code on GitHub

Code examples, hands-on labs, and project source code are available through a dedicated GitHub repository, allowing you to follow the implementations, experiment with the examples, and build the solutions yourself.

Master the technologies behind modern Large Language Models and start building intelligent, production-ready AI applications.

• Format: Paperback
• Language: English
• ISBN: B0HG87L8NH
• ISBN13: 9798194493814
• Release Date: August 2026
• Publisher: Independently published
• Length: 530 Pages
• Weight: 2.69 lbs.
• Dimensions: 11.0" x 1.2" x 8.5"