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 Description
If you work in DevOps, SRE, platform engineering, software delivery, operations, testing, or security, this book shows how large language models (LLMs) can reduce delivery friction, improve operational visibility, and support more reliable engineering workflows. Written by enterprise digital transformation and delivery specialists, it focuses on moving LLMs beyond isolated experiments into practical software delivery systems.
You will build the LLM foundations needed to understand modern AI systems, including language model evolution, Transformer architecture, GPT-style generation, and efficient fine-tuning techniques such as LoRA and QLoRA. The book then connects these foundations to enterprise-ready patterns such as retrieval-augmented generation (RAG), multi-agent systems, and platform-based AI assistance. Through operations, testing, coding, project management, and cybersecurity scenarios, you will see how LLMs can support log analysis, ticket handling, root cause analysis, test generation, code generation, risk management, and security workflows.
By the end of the book, you will understand how to move from model experimentation to practical AI-assisted delivery, evaluate where LLMs create measurable value across DevOps, SRE, and platform engineering workflows, and recognize the constraints, risks, and governance considerations involved.
What you will learn
- Apply RAG and multi-agent patterns to enterprise software delivery and platform engineering scenarios
- Use LLMs to support operations tasks such as log analysis, ticket handling, incident response, and root cause analysis
- Explore how LLMs can improve software testing, static analysis, vulnerability repair, and test automation workflows
- Apply code LLMs to development workflows, including code generation, completion, review support, and project-level coding tasks
- Use LLMs to support project management, delivery coordination, risk analysis, and cybersecurity workflows
- Evaluate the practical value, risks, and constraints of introducing LLMs into DevOps, SRE, and platform engineering environments
Who this book is for
This 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
- Understanding the Foundations of Large Language Models
- Understanding the Transformer Architecture Behind Large Language Models
- Tracing the Path from Bigram Models to GPT and ChatGPT
- Applying Efficient Fine-Tuning Techniques to LLMs
- Building Enterprise AI Applications with RAG and Multi-Agent Systems
- Building a Modern Software Delivery Foundation
- Applying LLMs in Intelligent Operations and Maintenance
- Applying LLMs in Software Testing
- Applying Code LLMs in Software Development
- Applying LLMs in Project Management
- Applying LLMs in Cybersecurity
Gu Huangliang is a senior DevOps and enterprise digital transformation specialist with extensive experience in engineering productivity and intelligent operations systems. He works at a licensed financial institution and is recognized as a Tencent Cloud Most Valuable Professional (TVP) and Alibaba Cloud Most Valuable Professional (MVP). He serves on multiple national-level expert committees related to digital transformation, fintech, and cloud standards, and is the author of the best-selling DevOps Authoritative Guide. He is also a core contributor to the DevOps Capability Maturity Model and Enterprise IT Operations Development White Paper, and a frequent speaker at major technology conferences.
Zheng Qingzheng is a senior researcher at the FinTech Research Center with a PhD in Computer Science from Durham University. Formerly an engineer at Huawei, he specializes in financial big data risk control and intelligent systems, with published research and patented innovations in applied AI technologies.
Niu Xiaoling is Chair of the DevOps Standards Working Group and Editor of DevOps international standards. She has contributed to over 20 national and international cloud and DevOps standards, including the DevOps Capability Maturity Model, and has led maturity assessments for more than 50 enterprise projects.
Che Xin is Deputy Director at the China Academy of Information and Communications Technology (CAICT), specializing in enterprise digital transformation, cloud computing standards, and intelligent operations. He leads research, standards development, and evaluation initiatives related to digital infrastructure and transformation maturity models.
