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This Month's Bestsellers
The Developer's Guide to AI
A Field Guide for the Working Developer
by Jacob Orshalick, Jerry Mannel Reghunadh, and Danny Thompson
June 2026, 320 pp.
ISBN-13:<br>9781718504769
Print Book and FREE Ebook, $59.99
Ebook (PDF, Mobi, and ePub), $47.99
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Contents
Download Chapter 1: Understanding Large Language Models
Your boss is pitching new AI features. Your team is buzzing about MCP servers. Job postings are asking for AI experience with RAG, vector databases, fine-tuning, and agents. You can feel the excitement. You see the potential. You may be wondering how to get started in AI without a data science degree. You’re in the right place.
The Developer’s Guide to AI gives working developers a practical path through the terminology, tools, and implementation patterns that matter. It shows you how to build with AI using the tools you already know: JavaScript, Python, APIs, SDKs, and databases.
By the end of this book, you’ll know how to:
Call LLM APIs and stream intelligent responses directly to your UI.
Engineer prompts that produce reliable, production-ready results.
Build RAG pipelines using vector databases to give AI access to your private data.
Fine-tune models with LoRA for specialized tasks like classification.
Deploy AI agents using tool-calling and the Model Context Protocol (MCP) to reason and act inside real workflows.
LLMs, RAG, LoRA, MCP, embeddings, and agents are not just intimidating buzzwords. They are the building blocks for the next generation of software.
Grab your code editor, bring your engineering instincts, and let’s build what’s next!
Author Bio<br>Jacob Orshalick has over 20 years in software development as an independent consultant for startups and Fortune 500 companies, leading high-impact projects and speaking regularly at conferences.
Jerry Mannel Reghunadh is a senior director with over
20 years in tech, spanning QA, product innovation, and solution architecture. He is known for mastering complex concepts and making them accessible.
Danny Thompson is a director of technology who works with Fortune 500 companies, teaches software developers worldwide, and hosts The Programming Podcast.
Table of contents<br>Acknowledgments
Preface
Introduction
PART I: GETTING STARTED WITH AI
Chapter 1: Understanding Large Language Models
Chapter 2: Building Your First LLM-Powered Application
Chapter 3: Python Essentials for LLMs and APIs
PART II: PROMPT ENGINEERING
Chapter 4: Fundamentals of Prompt Engineering
Chapter 5: Prompt Engineering Techniques
Chapter 6: Prompt Engineering in Code
PART III: VECTOR DATABASES AND RAG
Chapter 7: Vector Databases in Practice
Chapter 8: Designing a Retrieval-Augmented Generation System
PART IV: ADAPTING MODELS TO REAL-WORLD TASKS
Chapter 9: Why and When to Customize a Model
Chapter 10: Preparing Data for Fine-tuning
Chapter 11: Fine-Tuning Models in Practice
PART V: BUILDING AGENTIC SYSTEMS
Chapter 12: From Workflows to Autonomous Agents
Chapter 13: Building an Autonomous Agent
Chapter 14: Extending Agents with Tools
Afterword
Index
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