The AI Product Engineer: Building, Evaluating, and Iterating on LLM-Powered Features Inside Real Software Products

Lindqvist, Oskar; Ashford, Natalie; Marston, Julia

/ ISBN 13: 9798185270479

Verlag: Independently published, 2026

Print on Demand. Bestandsnummer des Verkäufers I-9798185270479

Inhaltsangabe:

Shipping an AI demo is easy.

Shipping AI features that survive real users, unpredictable inputs, latency constraints, hallucinations, product requirements, and production traffic is where things become difficult.

Most teams discover this gap too late.

A chatbot prototype works perfectly in a demo. Then users behave unexpectedly. Costs rise. Responses become inconsistent. Quality drifts over time. Features break under edge cases. And suddenly building with AI feels less like software engineering and more like controlled chaos.

The AI Product Engineer is a practical guide for developers, product managers, founders, AI engineers, and technical leaders who want to build reliable LLM-powered product experiences that work in production—not just in prototypes.

Rather than focusing only on prompts or model APIs, this book teaches the complete lifecycle of AI feature development: deciding what should become an AI feature, designing effective user experiences, measuring quality, iterating safely, and deploying systems that improve over time.

What You'll Learn

Identify High-Value AI Opportunities

Learn how to determine where AI creates genuine user value and where traditional software remains the better choice.

Scope and Design AI Features

Understand frameworks for turning ambiguous AI ideas into measurable product requirements and user experiences.

Build Better AI User Experiences

Design interfaces for streaming responses, partial outputs, confidence signals, human feedback loops, and user trust.

Evaluation-Driven Development

Move beyond intuition with structured evaluation systems that measure output quality, reliability, and business impact.

Testing AI Features Properly

Implement A/B testing strategies, benchmark datasets, human evaluation methods, and automated quality checks.

Production Reliability Patterns

Learn fallback strategies, guardrails, retry logic, latency management, and failure handling techniques.

Ship Without Breaking Production

Deploy AI features safely with observability, monitoring, experimentation frameworks, and continuous improvement loops.

Topics Covered

• Product Design for AI Features
• LLM Feature Scoping
• Prompt Engineering
• Streaming UX Patterns
• Human-in-the-Loop Systems
• Evaluation-Driven Development
• A/B Testing for AI
• Fallback and Recovery Strategies
• AI Reliability Engineering
• AI Observability
• Guardrails and Safety Systems
• Production Deployment Patterns
• User Feedback Systems
• Iterative Product Development

Who This Book Is For

• Product Engineers
• Software Developers
• Product Managers
• AI Engineers
• Startup Founders
• Technical Leads
• UX Designers
• Engineering Teams Building AI Products

Whether you're building AI copilots, assistants, content tools, workflow automation systems, or entirely new product experiences, this book provides practical frameworks and proven patterns that help you move from experiment to production.

Why This Book Matters

The future will not belong to companies that simply add AI features.

It will belong to teams that know how to build AI products users trust.

This book shows you how.

Build smarter. Measure rigorously. Ship confidently.

Bibliografische Details

Titel: The AI Product Engineer: Building, ...
Verlag: Independently published
Erscheinungsdatum: 2026
Einband: Softcover
Zustand: New