AI Engineering: Building Applications with Foundation Models by Chip Huyen

Product Details

About the Book

AI Engineering: Building Applications with Foundation Models by computer scientist, author, and former Stanford lecturer Chip Huyen is the definitive, practical guide for building real-world software on top of generative AI models.

As foundation models like large language and multimodal models transform AI from an esoteric, research-heavy discipline into an accessible development tool, this book outlines the shift from training models from scratch to leveraging existing APIs and model-as-a-service workflows. Huyen provides a comprehensive framework to navigate the modern AI stack, detailing how to go from a simple prototype to a scalable production application.

Key Topics Covered:

• The AI Engineering Stack: How AI engineering differs from traditional machine learning engineering.
• Evaluation-Driven Development: Proven strategies for evaluating open-ended models, tackling hallucinations, and utilizing the rapid "AI-as-a-judge" methodology.
• Prompt & Context Engineering: Best practices for writing effective prompts and managing model context windows.
• Retrieval-Augmented Generation (RAG) & Agents: How to ground models with external data sources, build functional autonomous agents, and determine when fine-tuning is necessary.
• Dataset Engineering & Deployment: Managing data quality, optimizing inference speed, lowering costs, and establishing feedback loops for continuous improvement.

Praise for the Book

"Every AI engineer building real-world applications should read this book. It's a vital guide to end-to-end AI system design, from model development and evaluation to large-scale deployment and operation." — Andrei Lopatenko, Director of Search and AI, Neuron7

"Chip Huyen gets generative AI. She is a remarkable teacher and writer whose work has been instrumental in helping teams bring AI into production." — Luke Metz, Co-creator of ChatGPT and Research Manager at OpenAI

Why Choose This Book?

• Written by an Industry Expert: Authored by Chip Huyen, a prominent AI infrastructure specialist and bestselling author of Designing Machine Learning Systems.
• Practical & Production-Focused: Focuses on real-world constraints like cost, latency, safety, evaluation metrics, and guardrails rather than heavy mathematical theory.
• Comprehensive Coverage: Spans the entire lifecycle of an AI app—from choosing a foundation model and structuring RAG pipelines to deploying scalable agents.
• Bridges the Gap: Excellent resource for cross-functional teams, helping software engineers, system architects, and product managers collaborate effectively on AI initiatives.

About the Author

Chip Huyen is a writer, computer scientist, and technology entrepreneur. Previously working on machine learning tooling at companies like NVIDIA and Netflix, she also founded an AI startup and taught Machine Learning Systems Design at Stanford University. Her books are widely recognized as industry essentials for modern AI practitioners.

Who Should Read This Book?

• Software Engineers & Developers: Backend and full-stack developers looking to transition into building robust AI-powered features and apps.
• System Architects & Technical Leads: Professionals designing scalable, reliable infrastructure around LLMs and multimodal models.
• Product Managers: Leaders wanting a technical grasp of what foundation models can achieve, how to evaluate them, and how to scope AI products.
• AI Enthusiasts & Students: Anyone eager to move beyond basic prompt experimenting and learn professional production deployment patterns.

Why Buy From Us?

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