Building AI Products from Scratch- Volume 1 : Foundations, Product Strategy, and System Architecture
Language: English
Published by Amazon Digital Services LLC - Kdp Sep 2026, 2026
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- Title
- Building AI Products from Scratch- Volume 1 : Foundations, Product Strategy, and System Architecture
- Author
- Mira S Devlin
- Publisher
- Amazon Digital Services LLC - Kdp Sep 2026
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 13
- 9798175131582
- Item weight
- 1,193 grams
- Dimensions
- 279x216x27 mm
- Series
- Book 1 of 2: Building AI Products from Scratch
Building AI Products from Scratch—Volume 1
Foundations, Product Strategy, and System Architecture
AI can generate impressive results in minutes. Building a real AI product takes engineering.
A model can answer questions, analyze documents, generate content, write code, and automate tasks. But a model alone is not a product. A production AI product requires a valuable problem, a defined outcome, thoughtful product design, reliable workflows, appropriate technology choices, and an architecture capable of evolving as AI changes.
Building AI Products from Scratch—Volume 1 provides a practical foundation for designing modern AI products from the ground up.
This book begins before implementation—at the point where builders must determine what to build, why it matters, and how the resulting system should be engineered.
What You Will LearnInside this volume, you will learn how to:
• Understand how modern AI products differ from traditional software
• Identify valuable AI product opportunities and evaluate feasibility
• Translate user problems into clear product requirements and outcomes
• Design product visions around users, workflows, and measurable value
• Architect AI systems across models, applications, APIs, data, tools, and infrastructure
• Select AI technologies and foundation models based on product requirements
• Evaluate trade-offs involving capability, cost, latency, reliability, control, and complexity
• Design AI workflows connecting user intent, context, intelligence, business logic, and execution
• Establish boundaries between probabilistic AI and deterministic software
• Design for uncertainty, failure, fallback behavior, and recovery
• Understand how context, data, tools, and system state influence AI behavior
• Build architectures that can evolve as models, platforms, users, and requirements change
A common mistake in AI product development is to begin with the question:
“What can this model do?”
Product engineering begins with a different question:
“What valuable problem can this system solve, for whom, under what constraints?”
That distinction drives better product decisions and stronger architecture.
This book treats an AI product as a complete engineered system, not simply a model connected to an interface. The model operates within a larger system containing users, applications, data, APIs, tools, workflows, policies, infrastructure, and controls.
Several principles are central throughout the book:
Model output is not automatically verified state.
AI capability is not execution authority.
Context is not automatically truth.
User intent is not authorization.
A prototype is not production.
Built for AI Product BuildersThis book is designed for AI/ML engineers, software engineers, full-stack developers, system architects, technical leaders, product managers, founders, engineering managers, and developers transitioning into AI engineering.
It is useful for teams building AI assistants, enterprise copilots, intelligent automation systems, knowledge applications, AI-powered SaaS products, and other AI-native applications.
Rather than focusing on one model or framework, the book emphasizes durable engineering principles and architectural reasoning that can remain useful as AI technologies evolve.
If you want to move beyond AI demos and learn how to approach AI applications as real engineered products, this is where the journey begins.
Start with the problem. Define the outcome. Design the system. Choose the technology. Engineer the workflow. Then build.
Mira S. Devlin
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