Overview

LLMs and Small Language Models focuses on how different model sizes influence the design and performance of AI applications. It presents a balanced view of when to use large language models and when smaller models offer a more practical solution. Rather than assuming that larger models are always better, the book examines how efficiency, responsiveness, and deployment constraints shape real-world decisions. The material explores how applications can be designed with model selection in mind, showing how different tasks benefit from different levels of model capability. It highlights how smaller models can provide speed and cost advantages, while larger models offer broader reasoning and flexibility. By understanding these tradeoffs, readers can make informed decisions about how to structure their systems for both performance and practicality. Attention is also given to how models are integrated into applications, including how they interact with retrieval systems, workflows, and user-facing features. The book presents model choice as part of a larger design process, where each component contributes to the overall behavior of the system. This perspective helps readers build applications that are not only capable, but also efficient and well suited to their intended environment.

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