A practical guide to applying LLMs across the software development and delivery lifecycle, improve development, testing, operations, and project efficiency across modern software organizations. Key Features Apply LLMs to modern DevOps workflows across development and operations with practical enterprise examples Build architectural fluency in GPT, fine-tuning, RAG, and agent-based systems Strengthen software delivery pipelines with AI-informed automation and operational intelligence Book DescriptionIf you work in software engineering, DevOps, SRE, or platform teams, this book written by enterprise digital transformation specialists demonstrates how large language models (LLMs) can enhance automation, software delivery, and operational reliability across modern engineering organizations. To build familiarity, the book begins hands-on with the technical underpinnings of LLMs, including Transformers, GPT architectures, and fine-tuning techniques such as LoRA and QLoRA. It then develops these foundations to demonstrate how retrieval-augmented generation (RAG) and agent-based systems can be embedded into real enterprise workflows. Across development, testing, operations, security, and project management scenarios, you will see how LLMs enhance code generation, automate testing, improve log analysis and incident response, support root cause analysis, and assist in risk-based decision-making. By the end of the book, you will be able to move from isolated model experimentation to scalable enterprise practice, designing intelligent DevOps and SRE workflows that are efficient, reliable, and strategically aligned. What you will learn Understand the evolution of large language models and Transformer-based architectures Build and optimize GPT-style models, including fine-tuning and reinforcement learning techniques Apply RAG and agent architectures to enterprise DevOps and platform engineering scenarios Use LLMs to automate operations tasks such as log analysis, ticket handling, and root cause analysis Enhance testing, programming, and CI/CD workflows with large language models Apply LLMs to project management, risk analysis, and security use cases in DevOps environments Who this book is forThis book is for software engineers, DevOps and SRE professionals, QA and security teams, and technical managers who want to apply and operationalize LLMs across the software delivery lifecycle.

Table of Contents:
Table of Contents

1. Introduction to Large Language Models
2. The Cornerstone of Large Language Models—Transformer
3. From Transformer to ChatGPT33
4. Fine-Tuning Techniques for Large Language Models
5. Enterprise AI Application Technology— RAG
6. Three Foundational Pillars of Software Delivery
7. Practical Applications of Large Language Models in Operations Scenarios
8. Practical Applications of Large Language Models in Testing Scenarios
9. Practical Applications of Large Language Models in Programming Scenarios
10. Practical Applications of Large Language Models in Project Management Scenarios
11. Practical Applications of Large Language Models in Security Scenarios