DeepSeek in Practice: From basics to fine-tuning, distillation, agent design, and prompt engineering of open source LLM

DeepSeek is more than a model name: understanding its architecture, reasoning behavior, and deployment choices can shape how an AI application works in practice. DeepSeek in Practice connects those foundations with hands-on development, taking readers from model concepts and prompting to agents, fine-tuning, and production deployment.

Andy Peng, Alex Strick van Linschoten, and Duarte approach the subject as both an AI system to understand and a tool to put to work. The book is aimed at technically experienced readers, with Python, APIs, and core machine-learning knowledge providing useful groundwork.

Understand what is happening inside DeepSeek

The opening material explores DeepSeek’s evolution, architecture, training, and reasoning. Topics include mixture-of-experts models, attention, reinforcement learning, reasoning patterns, multimodal capabilities, and the model’s limitations. This context helps readers think more clearly about what DeepSeek can do—and where trade-offs such as latency, interpretability, or deployment constraints may matter.

Make prompting part of the engineering

Rather than treating prompts as magic phrases, the book examines how to approach prompting DeepSeek models and how their behavior relates to underlying design choices. It also considers structured prompting and techniques for working with different model variants.

Move from model access to working applications

Practical sections address interacting with DeepSeek through APIs and local tools, developing applications, and exploring use cases such as coding and document understanding. Case studies include document and financial-document analysis, giving the discussion a concrete connection to real-world workflows.

Build agents, then plan for deployment

Agent-focused chapters cover tools, workflows, and the Model Context Protocol. Later sections consider fine-tuning and distillation, including a legal-reasoning example, before turning to deployment strategy, hardware and inference considerations, and production operations.

A technical read for builders 💻

This book is best suited to AI engineers, developers, and practitioners who already have some experience with Python, APIs, and machine-learning concepts. Its value lies in bringing model understanding and implementation into the same conversation—useful when deciding not only how to call DeepSeek, but how to build around it.