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The Developer's Guide to AI: A Field Guide for the Working Developer

by Jacob Orshalick

A practical route into building with generative AI

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AlbakiReads Editorial

A practical route into building with generative AI

Jacob Orshalick’s guide frames generative AI as an extension of everyday software development, with JavaScript, Python, APIs, SDKs, and databases as the working tools. Its coverage moves from calling language-model APIs and designing prompts to building retrieval-augmented generation pipelines, fine-tuning with LoRA, and deploying agents with tool-calling and the Model Context Protocol. The emphasis is on implementation patterns and terminology developers encounter when adding AI features, including ways to connect models with private data and real workflows. The book presents these topics as an applied path for software developers, without assuming a data science degree.

Good fit for readers who enjoy

  • Working developers seeking an introduction to generative AI implementation.
  • Programmers interested in LLM APIs, RAG, LoRA, or AI agents.
  • Developers who want to explore AI features using familiar languages and software tools.

Themes

Generative AI in software developmentLLM APIs and prompt engineeringRetrieval-augmented generation and vector databasesModel fine-tuning and AI agents

Based on publisher information and book metadata.

Book overview

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!
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Customer reviews

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Malaika Taylor

I highly recommend to for anyone looking for inner clarity

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Thomas DiMassa

Great service and fast service

Great shopping experience! The book arrived quickly, was well packaged, and matched the description perfectly. I would definitely shop with you again

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