The book in focus is AI Engineering: Building Applications with Foundation Models by Chip Huyen. Published by O’Reilly Media in 2025, this work aims to shed light on the process of constructing AI applications using foundation models.
AI Engineering by Chip Huyen is part of the growing body of literature that focuses on making AI more accessible for developers, engineers, and even non-technical professionals. The genre can be classified as AI development and engineering with a specific focus on practical, hands-on methodologies for integrating and deploying foundation models. These models, which include large language models like GPT-3, are transforming the AI landscape by reducing barriers to AI implementation. Huyen offers a deep dive into how these models can be applied, evaluated, and scaled across a range of industries.
Chip Huyen’s background gives her credibility in discussing AI engineering. Having worked at NVIDIA and Snorkel AI, she has extensive experience in machine learning and AI applications. Moreover, Huyen has authored a previous book titled “Designing Machine Learning Systems”, a best-seller in AI that has been translated into over 10 languages, showcasing her authority in the field.
The central thesis of AI Engineering is the exploration and practical implementation of foundation models—pre-trained AI models capable of performing a variety of tasks without needing to be retrained for each individual use case.
Huyen aims to equip readers with the tools and knowledge necessary to create scalable, efficient AI applications using these models. The book emphasizes AI engineering as an evolving discipline that differs significantly from traditional machine learning engineering, particularly in how AI applications are developed, deployed, and maintained.
Table of Contents
Background: Rise of AI Engineering
AI Engineering vs. ML Engineering
One of the key discussions in the book is the distinction between AI Engineering and traditional machine learning (ML) engineering. While both fields deal with the creation and deployment of AI systems, AI engineering is more focused on applying pre-trained foundation models rather than training models from scratch. This distinction is essential, as it highlights the shift from building models to adapting existing ones for real-world applications. According to Huyen, AI engineering focuses on model adaptation—tuning pre-trained models, creating appropriate user interfaces, and developing efficient deployment pipelines.
The book emphasizes that AI Engineering is not just about training models but about ensuring that they are used effectively in applications. Prompt engineering, finetuning, and retrieval-augmented generation (RAG) are among the key techniques discussed in the book, as they help engineers modify and tailor models without needing to retrain them entirely .
The Emergence of Foundation Models
The rise of foundation models has marked a paradigm shift in AI application development. These models, such as GPT or BERT, are not specialized in one specific task but are instead designed to handle a wide range of tasks with minimal adjustment. The availability of these models via APIs (such as OpenAI’s API) has democratized access to cutting-edge AI technologies, enabling developers, even those without a deep background in AI, to leverage them for their applications.
The low barrier to entry is another reason AI engineering has experienced rapid growth. As noted in the book, the use of foundation models via APIs enables developers to integrate powerful AI into applications with minimal coding. This has made AI development more accessible, allowing individuals and startups to create AI-driven solutions without requiring extensive infrastructure .
Summary of AI Engineering
Chip Huyen’s AI Engineering: Building Applications with Foundation Models is a comprehensive guide designed to demystify the complexities of using foundation models to build real-world AI applications. The book offers readers a step-by-step approach, starting from the fundamental concepts of AI Engineering and progressing through practical methods for deploying scalable AI systems. Below is an extended summary of the book’s main points, arguments, and lessons.
Main Themes of the Book
1. Understanding Foundation Models
A. Foundation models are large-scale, pre-trained models capable of performing a variety of tasks without needing task-specific training. Huyen emphasizes that these models have revolutionized the AI industry, particularly by offering applications in areas like natural language processing, image recognition, and even code generation.
B. The book delves into how these models are built, using a variety of data sources and self-supervised learning methods, making them robust enough to adapt to numerous applications.
C. One of the key aspects of foundation models is their ability to generalize across different tasks. Unlike traditional models, which are designed for specific tasks (e.g., translation or object recognition), foundation models are multitask-capable, making them highly versatile.
2. From AI Engineering to Full-Stack AI Systems
A. Traditional machine learning engineering focused on building models from scratch, often requiring considerable data preprocessing, feature engineering, and training from the ground up. AI Engineering, as Huyen defines it, revolves around adapting these pre-trained models, leveraging techniques like prompt engineering, RAG, and finetuning to fine-tune models for specific use cases.
B. The book also touches upon the integration of AI models into production environments and how these models are scaled efficiently. Unlike traditional machine learning, AI engineering involves working with models provided via API services, dramatically reducing the technical barriers to entry and allowing companies and developers to implement AI solutions faster.
3. Techniques and Tools for Adapting Models
Huyen goes deep into the various techniques required to adapt foundation models to particular tasks:
- Prompt Engineering: The process of crafting inputs to guide the model in generating the desired output.
- Retrieval-Augmented Generation (RAG): A method that combines the power of external data sources with the model’s generative capabilities to produce more accurate and contextually relevant responses.
- Finetuning: A process where pre-trained models are further trained on task-specific data to improve performance. The author explains how finetuning is a memory-intensive process and discusses alternative approaches for memory-efficient training.
4. Evaluating and Benchmarking AI Systems
A. A major focus of the book is on how to evaluate foundation models and their performance across tasks. Huyen stresses that evaluation is an ongoing challenge in AI engineering, and traditional metrics such as accuracy and precision may not fully capture the effectiveness of these models.
B. The book explores more advanced evaluation strategies, including functional correctness and similarity measurements. It provides readers with frameworks for building comprehensive evaluation pipelines that account for the unique challenges posed by foundation models.
C. One important point raised is that AI models, especially generative models, have certain limitations, including the propensity to generate hallucinations (incorrect or fabricated information). Huyen offers insight into how to identify and mitigate these issues, ensuring that AI applications remain reliable.
5. Deployment and Optimization
A. Building an AI application is only one part of the process. Deployment and optimization are equally critical to ensure that these applications perform at scale. Huyen provides actionable advice on optimizing model inference to reduce latency and costs, an essential consideration when serving large models to end-users.
B. She also addresses bottlenecks in serving foundation models, providing best practices for improving performance and scalability. Topics covered include AI accelerators, model optimization, and inference service optimization.
6. AI in the Real World: Use Cases and Future Trends
A. Huyen includes numerous real-world examples of AI applications in industries like healthcare, finance, customer service, and education. The book explores how different sectors can leverage foundation models to innovate, reduce costs, and improve user experience.
B. The author also offers future predictions for the evolution of AI engineering, including the increasing importance of multimodal models that can handle both text and images, as well as models that integrate even more complex forms of data like video and 3D models.
Chapter Breakdown and Structure
The book is logically structured to walk the reader through the lifecycle of AI application development, from the understanding of foundation models to the deployment of AI applications in production. Below is a high-level breakdown of how the book is organized:
- Chapter 1: Introduction to AI Engineering
- An overview of foundation models, AI engineering, and the new stack required for AI engineering. Huyen sets the context for the book by exploring the evolution from language models to foundation models and how AI engineering differs from traditional ML engineering.
- Chapter 2: Understanding Foundation Models
- A deep dive into the architecture and mechanics of foundation models, including training data, multilingual models, and domain-specific adaptations. This chapter is foundational for understanding the technical side of AI engineering.
- Chapter 3: Evaluation Methodology
- This chapter discusses the difficulties of evaluating foundation models and offers methods for measuring their effectiveness in various contexts, from language understanding to image generation.
- Chapter 4: Evaluate AI Systems
- Huyen extends the discussion on evaluation and introduces domain-specific capabilities, performance metrics, and strategies for evaluating the cost and latency of AI models in production.
- Chapter 5: Prompt Engineering
- Focuses on the critical skill of prompt engineering, teaching how to effectively communicate with foundation models using well-crafted prompts that ensure accuracy and relevance.
- Chapter 6: RAG and Agents
- Explores the integration of external data with foundation models through RAG and discusses agentic systems that leverage AI models to interact autonomously with users and other systems.
- Chapter 7: Finetuning
- Discusses when and how to finetune models, considering both the memory constraints and data requirements involved in this complex process.
- Chapter 8: Dataset Engineering
- A practical guide to collecting, curating, and processing the right data for training and fine-tuning foundation models, emphasizing the importance of data quality and quantity.
- Chapter 9: Inference Optimization
- Covers techniques to optimize the inference process for faster and more cost-effective AI applications, providing methods for improving both the model and the service hosting the model.
- Chapter 10: AI Engineering Architecture and User Feedback
- The final chapter ties together the earlier chapters by discussing the end-to-end architecture for deploying AI systems and how to use user feedback to continuously improve AI applications.
Summary of Key Lessons
AI Engineering focuses on adapting powerful, pre-trained foundation models to solve specific problems, whereas traditional ML engineering involves building models from scratch.
Foundation models are highly versatile and capable of handling multiple tasks without being retrained for each one, making them invaluable tools for AI engineers.
- Key techniques for adapting foundation models include prompt engineering, RAG, and finetuning, each of which plays a crucial role in making models suitable for production environments.
- Proper evaluation and optimization are essential in ensuring that AI applications perform well in real-world scenarios, with special attention paid to cost, latency, and model accuracy.
Critical Analysis of “AI Engineering” by Chip Huyen
Chip Huyen’s AI Engineering: Building Applications with Foundation Models provides a comprehensive and highly practical framework for navigating the rapidly evolving field of AI engineering. This analysis will evaluate the content, style, themes, relevance, and the author’s expertise in the domain.
Evaluation of Content
AI Engineering excels in presenting the core principles behind AI application development using foundation models. Huyen does a remarkable job of covering the breadth of topics essential for building AI systems, from foundation models and model evaluation to deployment and optimization. The book is replete with practical advice, derived from Huyen’s real-world experiences and backed by examples from multiple industries.
One of the standout strengths of the book is its balance between theory and practice. Unlike many AI books that delve deep into technical theory without offering actionable insights, AI Engineering bridges this gap by offering a hands-on approach that is directly applicable to developers, engineers, and businesses looking to implement AI at scale. For example, Chapter 5’s focus on prompt engineering is an excellent resource for those seeking to improve interaction with foundation models, providing a clear path to improving output relevance.
Moreover, Huyen introduces key concepts such as RAG (Retrieval-Augmented Generation) and finetuning, making them easy to understand while highlighting their significance in fine-tuning models to suit specific use cases. The discussions on model evaluation and metrics are also valuable, as they address the challenges in assessing AI models and ensure their real-world applicability.
However, one critique is the limited focus on the challenges associated with scaling AI applications in real-world environments, particularly in cases where models are deployed at massive scales. While the book discusses optimization and inference, more emphasis could have been placed on the specific pitfalls that engineers may face during large-scale deployment, such as issues with model drift or continuous integration in production.
Style and Accessibility
Huyen’s writing style is clear, engaging, and approachable, which is especially important considering the technical depth of the material. The language is concise, and the book is structured logically, making complex AI concepts more digestible for both newcomers and experienced engineers.
Her use of real-world case studies and practical examples makes the content feel relevant and applicable. For instance, the sections that illustrate the applications of foundation models in industries like healthcare, customer service, and marketing provide concrete examples of how AI is transforming sectors and solving real-world problems.
The visual aids, including charts and model architecture diagrams, enhance understanding, particularly in sections that discuss model architectures and evaluation pipelines. The modular structure of the book allows readers to jump between topics without losing context, making it an excellent resource for professionals who need to refer to specific sections.
However, despite its accessibility, some of the more technical sections—especially the chapters on finetuning and data engineering—may prove overwhelming for beginners without a background in machine learning. While the book provides sufficient high-level explanations, more introductory material on the mathematical foundations behind some of the concepts could have made the book more accessible to readers with little or no background in AI.
Themes and Relevance
The book’s themes revolve around the adaptability of AI models, model optimization, and the scaling of AI applications. These themes are highly relevant in today’s rapidly evolving AI landscape. The focus on foundation models aligns with current industry trends, where businesses and developers are increasingly turning to these pre-trained models to quickly build AI applications.
Huyen does a great job of addressing the scalability of AI, especially with the rise of multimodal models and the low-barrier-to-entry nature of AI application development. By discussing the AI engineering stack and contrasting it with traditional ML engineering, the book highlights how new AI development practices are making AI more accessible and less resource-intensive.
The inclusion of ethical considerations, such as the challenges with model biases and hallucinations, underscores the relevance of the book in today’s discussions on responsible AI development. Huyen encourages engineers to think critically about the limitations of AI systems and how to mitigate risks, such as misinformation or harm caused by flawed AI models.
However, the book could benefit from a deeper dive into the broader social implications of AI. While it touches on issues such as data privacy and model biases, it does not fully explore the ethical frameworks or regulatory landscapes that companies must navigate as they build and deploy AI. These considerations are becoming increasingly important, especially with growing concerns around data privacy laws and the ethical use of AI.
Author’s Authority
Chip Huyen’s authority on the subject matter is unquestionable. Her experience at Snorkel AI and NVIDIA, along with her work on machine learning systems design at Stanford, lends her deep credibility in the AI engineering field. Moreover, Huyen’s hands-on experience with real-world projects and her ability to translate these experiences into practical advice for engineers is a testament to her expertise.
The depth of knowledge she brings to the table is evident in her discussions about model training, prompt engineering, finetuning, and evaluation pipelines, all of which require a solid understanding of the AI and machine learning landscape. Her ability to distill complex ideas into actionable insights makes the book invaluable for both practitioners and those seeking to enter the field.
Strengths and Weaknesses
Strengths
Comprehensive Framework: The book provides a holistic framework for building AI applications, from model selection and finetuning to deployment and optimization. This makes it suitable for engineers at different stages of the AI development process.
Practical Examples: Huyen’s use of real-world case studies allows readers to see how AI models are applied across industries. These examples serve as a practical guide for applying the book’s teachings.
Clear, Engaging Writing: The book is approachable and easy to read, even for non-experts, while still providing technical depth for experienced engineers.
Weaknesses
Overwhelming for Beginners: Some sections, particularly those discussing finetuning and data engineering, may be too complex for readers with little to no background in machine learning.
Limited Ethical Considerations: The book does not delve deeply into the ethical implications of deploying foundation models, which is an increasingly important topic in the AI community.
Lack of Focus on Large-Scale Deployment Pitfalls: While the book covers optimization and inference, it does not provide in-depth guidance on overcoming the challenges associated with scaling AI applications in large, production environments.
Reception/Criticism/Influence
Since its release, AI Engineering has received positive feedback from professionals across the AI and machine learning sectors. Vittorio Cretella, former global CIO at P\&G, praised the book for its well-structured approach to AI engineering, calling it a must-read for professionals looking to scale AI. Similarly, Luke Metz, co-creator of ChatGPT, lauded the book for being a comprehensive guide to building generative AI applications in production.
Critics have highlighted the book’s practical nature, with many appreciating its actionable advice and the step-by-step approach. However, some have pointed out that the book’s technical complexity may limit its appeal to beginners in the field, especially those who are unfamiliar with AI engineering fundamentals.
Comparison with Similar Works
In AI Engineering, Chip Huyen offers a comprehensive and practical guide to working with foundation models, a growing area in AI. However, several other works tackle similar themes, though with varying perspectives or focuses. Below is a comparison with other well-regarded books in the AI engineering and machine learning fields:
1. Designing Machine Learning Systems by Chip Huyen
As Huyen’s previous work, Designing Machine Learning Systems (DMLS) is naturally a close comparison to AI Engineering. While both books discuss building AI applications, they are centered on different paradigms. DMLS is focused primarily on traditional machine learning, including tabular data, model training, and feature engineering. It deals with training machine learning models from the ground up, a process that requires a solid understanding of data pipelines and model building techniques.
AI Engineering, on the other hand, zeroes in on foundation models—large, pre-trained models that can be adapted to various tasks with minimal modification.
This represents a shift away from the ground-up model-building approach toward leveraging existing models in novel ways. Therefore, while DMLS is ideal for ML engineers focusing on building custom models, AI Engineering is more suited to AI engineers and developers working with off-the-shelf solutions and adapting them to real-world applications.
2. Hands-On Machine Learning with Scikit-Learn, Keras, and Tensor Flow by Aurélien Géron
This book is one of the most popular resources for beginners and intermediate learners in machine learning. Hands-On Machine Learning provides an introduction to machine learning concepts and practical applications using tools like Scikit-learn, Keras, and TensorFlow. Unlike AI Engineering, which focuses on foundation models and large-scale AI systems, Géron’s book emphasizes building models from scratch, exploring fundamental concepts like neural networks, deep learning, and convolutional networks.
For beginners interested in hands-on machine learning, this book is an excellent starting point. However, it does not delve into foundation models or the specialized knowledge required for AI Engineering. AI Engineering is therefore more advanced, catering to readers who wish to understand how to adapt and deploy pre-trained, large models at scale.
3. “Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
Deep Learning is the authoritative textbook on deep learning principles, widely considered the “bible” of the deep learning field. The book provides an in-depth exploration of the theoretical foundations of deep learning, covering neural networks, optimization techniques, and backpropagation.
While this book is foundational for anyone pursuing an in-depth understanding of how deep learning algorithms work, it does not directly address the practical aspects of engineering AI applications or adapting foundation models.
In contrast, AI Engineering is specifically focused on applying foundation models—such as GPT-4 and CLIP—in real-world settings, providing practical frameworks for adapting these models to specific use cases. While Deep Learning offers the theoretical grounding, AI Engineering provides the practical tools and strategies for AI application development.
4. Artificial Intelligence: A Guide for Thinking Humans by Melanie Mitchell
Mitchell’s book is a highly accessible exploration of AI’s capabilities, history, and future implications. It offers a non-technical look at AI and critically examines its limitations and ethical challenges. Unlike AI Engineering, which is a technical manual for practitioners, Mitchell’s book serves as an overview for the general public, explaining AI’s potential while highlighting its current shortcomings.
While AI Engineering focuses on the practical aspects of engineering AI systems, Artificial Intelligence: A Guide for Thinking Humans focuses more on the philosophical and ethical questions surrounding AI’s development and deployment. Huyen’s book offers the tools and techniques needed to build AI solutions, while Mitchell’s addresses the broader impact of AI on society.
5. AI Superpowers: China, Silicon Valley, and the New World Order by Kai-Fu Lee
This book examines the global AI race, focusing on the developments in both China and Silicon Valley, and the economic, social, and political implications of AI. Lee provides a macro perspective on AI’s development, contrasting how different countries approach AI technology. While AI Superpowers does not provide practical engineering methodologies for building AI systems, it provides critical insights into global AI trends and geopolitical implications.
In comparison, Huyen’s AI Engineering focuses on building and scaling AI applications from a technical standpoint, offering practical engineering solutions that can be applied across industries. Lee’s book complements AI Engineering by offering insight into the broader AI ecosystem, which can be useful for understanding the global market that engineers are building for.
Conclusion
AI Engineering: Building Applications with Foundation Models by Chip Huyen is an essential resource for AI engineers and anyone looking to develop real-world AI applications using foundation models. The book strikes a balance between theory and practical advice, making it suitable for both beginners and experienced engineers who are ready to work with powerful AI models such as GPT, CLIP, and others.
Compared to similar works, AI Engineering is unique in its focus on foundation models and AI application development. While other books, such as Deep Learning by Goodfellow and Hands-On Machine Learning by Géron, focus on foundational concepts or specific machine learning frameworks, AI Engineering focuses on engineering the next generation of AI systems using pre-trained models. This makes it especially relevant for those aiming to deploy AI at scale.
For those interested in a hands-on guide to building and deploying AI applications with pre-trained models, Chip Huyen’s book is indispensable. It serves as a crucial guide to navigating the rapidly evolving world of AI Engineering and provides the tools needed to build AI-driven solutions efficiently.
In the next sections, I will summarize the overall impression of the book, restate its strengths and weaknesses, and offer a recommendation for potential readers.
