Small Language Models for Efficient AI

Small Language Models are reshaping how practical AI systems are built. When latency, cost, privacy, memory, and deployment flexibility matter as much as raw model capability, smaller models can offer a better engineering fit - but only when they are designed, trained, evaluated, and integrated with discipline.

Small Language Models for Efficient AI provides a practical guide to the complete SLM lifecycle. It explains how to choose the right model strategy, prepare effective data, train or adapt compact models, align desired behaviour, evaluate quality and efficiency, and optimize inference for production. It also shows how SLMs can operate within larger AI systems through LLM fallback, retrieval, tool use, agentic workflows, knowledge augmentation, and edge deployment.

Written for AI engineers, forward-deployed engineers, applied scientists, the book focuses on practical decisions, repeatable workflows, and production trade-offs rather than theory alone. Practical case studies and reference implementations translate the book’s engineering principles into adaptable solution patterns that readers can extend to their own domains, workloads, and deployment environments. 

By the end, readers will understand where SLMs provide the most value, how to build and optimize them effectively, how to recognize their capability boundaries, and how to engineer reliable AI systems that combine efficiency with the right level of intelligence.

What You Will Learn

• Deciding when to use SLMs based on capability, cost, latency, privacy, data, and deployment constraints

• Designing effective SLM systems across architecture, tokenization, data strategy, retrieval, tools, agents, guardrails, and system boundaries

• Developing capable SLMs through training, distillation, fine-tuning, preference optimization, domain adaptation, evaluation, and optimization

• Deploying reliable SLM solutions using quantization, efficient inference, routing, observability, controlled execution, recovery, and edge deployment

• Applying SLM engineering in practice through case studies, reference code, and reusable implementation patterns for real-world projects

Who this book is for:

This book is for AI engineers and applied scientists developing compact language models for production use. It also serves practitioners and technical teams deploying efficient, cost‑effective SLM solutions across enterprise and edge environments. 

Santanu Bhattacharjee is Director of AI and Product Engineering at Pretium, with more than 15 years of experience in artificial intelligence, machine learning, and product engineering. Previously, he served as Senior Chief Engineer at Samsung Research, where he contributed to the research and development of advanced AI systems. His work spans building small language models for agentic systems, deploying SLMs on edge devices, and integrating language models with knowledge graphs. He has deep expertise in model distillation, SLM development, agentic AI systems, and scaling AI solutions in production. Santanu holds a BE from Jadavpur University and an MS from Dublin City University. His research includes patents and publications on episodic knowledge graph construction for edge devices, natural language querying for big data platforms. He has received multiple recognitions as an AI engineering leader.