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Artículo: AMZ-B0G34ZRH56
Programming LLMs with DSPy: Architect Self-Improving Agents and RAG Workflows for Maximum Accuracy and Scale
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0.81 kg
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Sobre este producto
- Are you tired of the "Prompt Engineering" grind? You spend hours tweaking strings of text, adjusting temperatures, and crossing your fingers, only to watch your application break when the model updates. You’ve built a prototype that works 80% of the time, but falls apart in production. You are stuck in a cycle of brittle maintenance, fighting against the stochastic nature of Large Language Models. It is time to stop guessing and start programming. "Programming LLMs with DSPy" is the definitive guide to the Stanford NLP DSPy framework—the revolutionary tool that is rendering manual prompt engineering obsolete. Written for Python developers, AI engineers, and data scientists, this book moves you beyond the hype of simple chatbots and into the era of Neuro-Symbolic Programming. It teaches you how to treat LLMs not as black boxes, but as modular, optimizable software components. Inside, you will discover how to:Ditch Brittle Prompts: Replace messy string manipulation with clean, declarative Signatures and Modules.Build Robust RAG Systems: Architect production-grade Retrieval-Augmented Generation pipelines using multi-hop search, "Baleen" architectures, and re-ranking strategies.Automate Optimization: Use the DSPy Teleprompter and BootstrapFewShot optimizer to mathematically tune your prompts, achieving higher accuracy with zero manual tweaking.Architect Autonomous Agents: Design self-improving agents capable of ReAct loops, reflection, and self-correction.Slash Latency & Cost: Implement Cross-Model Compilation to distill the intelligence of GPT-4 into faster, cheaper models like Llama 3 or Haiku.Master the Feedback Loop: Build self-healing systems that capture user feedback to continuously retrain and improve your agents in production.Why This Book? While other resources focus on "prompt hacks," this book focuses on System Design. Whether you are looking for a LangChain alternative or seeking to build enterprise-scale Generative AI applications, this guide provides the engineering rigor you have been missing. Who This Is For:Software Engineers ready to bridge the gap between Python logic and AI intuition.Data Scientists looking to move from Jupyter notebooks to high-throughput production APIs.CTOs and Architects designing scalable NLP development stacks.The future of AI isn't about writing better prose; it's about curating better data. Join the revolution. Scroll up and grab your copy today to start programming the future of intelligence.
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