Textbook in PDF format Large language models (LLMs) have transformed natural language processing, but deploying them in applications introduces numerous technical challenges. Large Language Models: The Hard Parts offers 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 systems Manage input formatting and long-context retrieval Address output inconsistency and structural unreliability Implement safety and content moderation frameworks Explore alignment challenges and mitigation techniques Leverage open source models and optimize costs Brief Table of Contents (Not Yet Final) The Evals Gap (available) Open-Source Evaluation Frameworks (available) Unstructured Input Data (available) Structured Data Output (unavailable) Laws, Guidelines, and Safety Techniques (unavailable) Safety Tools (unavailable) Policy Alignment (unavailable) Local and Open-Source LLMs (unavailable) Frontiers (unavailable)