Developer's Guide to AI

Nyhet

Häftad, Engelska, 2026

AvJacob Orshalick,Jerry Mannuel Reghunadh,Danny Thompson,Jerry M. Reghunadh

759 kr

Kommande

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.

Produktinformation

Utgivningsdatum2026-06-09
Mått178 x 235 x undefined mm
Vikt368 g
FormatHäftad
SpråkEngelska
Antal sidor320
FörlagNo Starch Press,US
ISBN9781718504769

Tillhör följande kategorier

Artificiell intelligens inom Data och IT
Programspråk inom Data och IT

Jacob Orshalick has over 20 years in software development, including 16+ as an independent consultant for startups to Fortune 500 companies. A seasoned technologist, he’s led high-impact projects, revitalized struggling initiatives, and co-authored Seam Framework: Experience the Evolution of Java EE. Orshalick speaks regularly at conferences and shares his work at focus.dev.Jerry Mannel Reghunadh is a Senior Director with 20+ years in tech, starting in QA before moving into product innovation and solution architecture. Known for mastering complex concepts and making them accessible, he’s a frequent conference speaker and lifelong tinkerer. Jerry shares his projects at jerrymannel.me. Danny Thompson is a software developer who now leads as a Director of Technology, working with Fortune 500 companies and teaching software developers worldwide. Host of The Programming Podcast and organizer of The Commit Your Code Conference, he is a sought-after speaker who specializes in making complex topics very approachable.

AcknowledgmentsPrefaceIntroductionPART I: GETTING STARTED WITH AIChapter 1: Understanding Large Language ModelsChapter 2: Building Your First LLM-Powered ApplicationChapter 3: Python Essentials for LLMs and APIsPART II: PROMPT ENGINEERINGChapter 4: Fundamentals of Prompt EngineeringChapter 5: Prompt Engineering TechniquesChapter 6: Prompt Engineering in CodePART III: VECTOR DATABASES AND RAGChapter 7: Vector Databases in PracticeChapter 8: Designing a Retrieval-Augmented Generation SystemPART IV: ADAPTING MODELS TO REAL-WORLD TASKSChapter 9: Why and When to Customize a ModelChapter 10: Preparing Data for Fine-tuningChapter 11: Fine-Tuning Models in PracticePART V: BUILDING AGENTIC SYSTEMSChapter 12: From Workflows to Autonomous AgentsChapter 13: Building an Autonomous AgentChapter 14: Extending Agents with ToolsAfterwordIndex