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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!
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!
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
| Erscheinungsjahr: | 2026 |
|---|---|
| Genre: | Importe, Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Inhalt: | Einband - flex.(Paperback) |
| ISBN-13: | 9781718504769 |
| ISBN-10: | 1718504764 |
| Sprache: | Englisch |
| Einband: | Kartoniert / Broschiert |
| Autor: |
Orshalick, Jacob
Reghunadh, Jerry M. Thompson, Danny |
| Hersteller: |
Random House LLC US
No Starch Press |
| Verantwortliche Person für die EU: | Libri GmbH, Europaallee 1, D-36244 Bad Hersfeld, gpsr@libri.de |
| Maße: | 234 x 180 x 25 mm |
| Von/Mit: | Jacob Orshalick (u. a.) |
| Erscheinungsdatum: | 09.06.2026 |
| Gewicht: | 0,591 kg |
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