Artículo: AMZ-B0G93DVWGW

Engineering LLMs with DSPy: Designing Declarative Prompt Systems, Self-Improving AI Pipelines, and Reliable Language Model Workflows

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Kindle

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0.20 kg
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Sobre este producto
  • In an era where language models are transforming industries, the difference between fragile prototypes and production-grade AI systems lies in systematic engineering. Engineering LLMs with DSPy is the definitive guide for practitioners who demand more than ad-hoc prompting. It introduces DSPy—a revolutionary declarative framework that transforms prompt engineering from an art into a science. Through nine meticulously structured chapters, this book teaches you how to: Build modular, composable prompt programs using DSPy’s signature-based declarative paradigm Design self-improving AI pipelines that automatically bootstrap and optimize their own prompts Engineer reliable, production-ready language model workflows with formal verification, robustness testing, and error-handling guarantees Optimize DSPy systems at scale using advanced, resource-efficient techniques Integrate DSPy with retrieval-augmented generation, multimodal models, and enterprise APIs Evaluate and benchmark DSPy pipelines with rigorous metrics and statistical rigor Whether you’re a machine learning engineer, AI researcher, or technical leader building the next generation of intelligent systems, this book provides the architectural patterns, optimization strategies, and best practices you need to move beyond brittle chains of prompts to robust, maintainable, and self-improving language model pipelines. Written by Hawke Calen, a leading expert in declarative AI programming and DSPy, this book bridges theory and practice with real-world case studies from enterprise chatbots, medical text analysis, financial forecasting, and fraud detection. Master the future of AI engineering with DSPy.

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