DSPy Agent Engineering: Build Modular, Self-Improving AI Systems with Declarative Python - Softcover

Inhaltsangabe

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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