Building Production-Grade LLMs

is the definitive manual for LLM engineers, AI architects, platform engineers, ML engineers, and technical leads seeking to build robust, scalable, and maintainable Large Language Model systems. Unlike tutorials or toy demos, this book is engineering-first and system-focused, offering deep insight into real-world production challenges. Covering the full lifecycle-from problem definition, data strategy, and model selection to deployment, observability, safety, and long-term operation-it guides readers through the design decisions, trade-offs, and architectures required to operate LLMs reliably at scale. including reference architectures, retrieval-augmented generation, agentic systems, performance optimization, governance, and case studies, this book equips practitioners with strategies for building production-grade LLMs that survive the complexities of real-world environments. Fully original, code-free, and scenario-driven, it serves as a long-term reference for serious LLM builders, emphasizing reliability, scalability, safety, and operational excellence.

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