LLM Design Patterns for Production Systems: Building Robust, Secure, and Cost-Efficient AI Applications with GPT, Claude, and Open-Source Models - Tapa blanda

Sinopsis

Large Language Models (LLMs) have rapidly evolved from experimental tools into core components of modern software systems. Organizations across industries are deploying LLM-powered applications for search, automation, customer support, analytics, and decision assistance. Yet while building a compelling demo is relatively easy, delivering an LLM system that is reliable, secure, scalable, and cost-efficient in production remains a significant challenge.
Unlike tool-specific tutorials, this book is intentionally model- and framework-agnostic. While examples reference popular platforms and libraries, the design patterns themselves are durable and adaptable, enabling teams to evolve their systems as models, vendors, and ecosystems change.
LLM Design Patterns for Production Systems is written for software engineers, AI engineers, technical leads, architects, and product teams who are building or maintaining LLM-powered applications at scale. It assumes basic familiarity with software development and LLM usage, but does not require deep machine learning expertise. The focus is on system design, operational reliability, and sound engineering judgment.

Whether you are deploying your first production LLM application or refining an existing system, this book equips you with the patterns and perspectives needed to build AI systems that are not only powerful—but dependable, secure, and ready for the long term.

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