Large language models (LLMs) have transformed natural language processing, but deploying them in applications introduces numerous technical challenges. Large Language Models: The Hard Partsoffers a clear, practical examination of the limitations developers and ML engineers face when building LLM-powered applications. With a focus on implementation pitfalls (not just capabilities) this book provides actionable strategies supported by reproducible Python code and open source tools. Readers will learn how to navigate key obstacles in system integration, input management, testing, safety, and cost control. Designed for engineers and technical product leads, this guide emphasizes practical solutions to real-world problems and promotes a grounded understanding of LLM constraints and trade-offs. Design testing strategies for nondeterministic systemsManage input formatting and long-context retrievalAddress output inconsistency and structural unreliabilityImplement safety and content moderation frameworksExplore alignment challenges and mitigation techniquesLeverage open source models and optimize costs