The Developer's Guide to AI (Paperback)

Jacob Orshalick

ISBN 10: 1718504764 / ISBN 13: 9781718504769

Published by No Starch Press,US, San Francisco, 2026

About this Item

Paperback. Working developers everywhere now use AI tools, but most still struggle to turn prompts and prototypes into reliable software features. The Developer's Guide to AI teaches engineers how to build real, maintainable AI capabilities using the tools they already know-JavaScript, Python, APIs, vector search, and simple agent workflows without advanced math or machine learning. Readers learn where AI belongs in an application, how to design around real-world constraints like latency and cost, and how to avoid the brittle prototypes that fail under changing requirements. With clear examples and production-focused patterns, this book helps developers ship AI features that last. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Seller Inventory # 9781718504769

Bibliographic Details

• Title: The Developer's Guide to AI (Paperback)
• Publisher: No Starch Press,US, San Francisco
• Publication Date: 2026
• Language: English
• ISBN 10: 1718504764
• ISBN 13: 9781718504769
• Binding: Paperback
• Condition: new

About this title

Synopsis

Build intelligent applications—no data science degree required.

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!

About the Author

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.