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Large Language Models: The Hard Parts

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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 AI engineers face when building LLM-based 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 application evaluation, input management, testing, and safety. Designed for builders 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 and evaluation strategies for nondeterministic systemsManage context, RAG, and long-context retrievalAddress output inconsistency and structural unreliabilityImplement safety and content moderation frameworksExplore alignment challenges and mitigation techniquesLeverage open source models locally

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