Overview

Are you tired of fragile AI systems that break under real-world conditions? Endless prompt tuning, inconsistent outputs, and brittle workflows can make AI development feel like guesswork. It's time to take control. DSPy Agent Engineering introduces a revolutionary approach to building robust, modular, and self-improving AI agents using declarative Python programming. Instead of writing fragile prompts, DSPy lets you define precise input-output behavior, orchestrate complex workflows, and implement automated optimization loops-so your AI continuously improves without manual intervention. Inside this book, you will learn how to: Construct reusable zero-shot and few-shot predictive modules. Implement retrieval-augmented generation (RAG) pipelines for scalable knowledge access. Build tool-using and multi-agent systems that interact seamlessly with APIs and external data. Deploy automatic optimization loops like BootstrapFewShot and MIPRO to enhance accuracy and efficiency. Integrate multimodal capabilities, human-in-the-loop feedback, and enterprise-grade monitoring. Packed with hands-on examples, best practices, and real-world case studies, this book is perfect for AI engineers, Python developers, and technical leaders who want to: Build production-ready AI agents that scale. Reduce development time while improving reliability. Master declarative approaches to AI that go beyond fragile prompts. Stop fighting brittle AI systems and start building intelligent, self-improving, and resilient agents. With DSPy, you're not just coding you're engineering the future of AI.

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