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.

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Listeners 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.

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Discover how to design testing and evaluation strategies for nondeterministic systems; manage context, RAG, and long-context retrieval; address output inconsistency and structural unreliability; implement safety and content moderation frameworks; explore alignment challenges and mitigation techniques; and leverage open source models locally.

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Large Language Models: The Hard Parts

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