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It is truly an honor to receive a request to write a foreword for a book like this, in this particular space, at this specific time in history, and by an author who I believe will make significant contributions to this industry during his tenure. That book is Domain-Specific Small Language Models, the space is generative AI, the time in history is the tremendous evolutionary acceleration of AI, and the author is Guglielmo Iozzia. In an era when AI discourse is dominated by ever-larger frontier models and eye-catching demos, this book takes a refreshingly pragmatic stance: it is about making language models actually work for your domain, your data, and your hardware constraints. It is written with the practitioner’s learning curve squarely in mind, moving from foundational concepts to hands-on experience so that by the time you turn the last page, you will not only understand why small, domain-specific language models matter, you will have run them, tuned them, optimized them, and deployed them on real-world infrastructure. What distinguishes this work is its relentless focus on the realities of distributed and edge AI. Rather than treating GPUs in the cloud as an infinite resource, the author leans into the constraints most organizations actually face: tight budgets, on-prem clusters, laptops, and even mobile or embedded devices. From the opening chapters on the definition and role of small language models (SLMs), it becomes clear that the goal is not to worship scale, but to show how carefully chosen, well-tuned models in the 100M to 10B parameter range can deliver outsized business value when pushed as close as possible to where data is generated and decisions are made. The walkthroughs of ONNX, quantization techniques, and hardware-aware optimizations anchor the discussion in concrete practices that materially reduce latency and cost at the edge while preserving useful accuracy. This is also an excellent grounding text for anyone trying to navigate the LLMversus- SLM decision space with intellectual honesty. The early chapters provide a clear, hype-free tour of the Transformer architecture, training paradigms, open source ecosystems, and the tradeoffs between generalist closed LLMs and domain-specific models. From there, the book steadily builds into a toolkit: data preparation, classic fine-tuning, PEFT methods such as LoRA, and retrieval-augmented generation are all presented with enough depth and runnable code that you can reproduce, adapt, and extend the examples for your own environment. The result is a rare combination of conceptual clarity and practical guidance that serves both as an introduction for those new to the field and as a structured reference for experienced practitioners formalizing their approach. Perhaps most compelling is the way the book repeatedly returns to domain reality. Coding assistants are explored not as abstractions but via end-to-end fine-tuning of GPT-2 on Manim code, complete with hyperparameter search and evaluation workflows that mirror production-grade ML engineering. Chemistry and life sciences are treated with equal seriousness, from protein and antibody sequence generation to discussion of validation workflows and specialized tooling, illustrating how SLMs unlock high-value use cases in regulated, data-sensitive industries. Later chapters connect these capabilities to modern application patterns—RAG pipelines, agentic systems, deployment and serving patterns, and running on commodity hardware—so that readers finish with a mental model that spans from theory to architecture to code. If you are serious about applying generative AI beyond glossy prototypes—especially in domains where data is sensitive, regulation is strict, or infrastructure is constrained— this book is worthy of your time. It will not just tell you that small language models are important; it will show you, step by step, how to build, adapt, and ship them responsibly in the environments where they matter most.