Overview

using Stanford's DSPy framework. In this in-depth exploration, Alex Ming introduces you to a declarative approach to AI one where agents are not just coded but taught to reason, evaluate, and improve. You'll discover how to: Build modular, reusable agent pipelines that integrate multiple reasoning components Implement contextual memory systems for persistent understanding Integrate LLMs to enhance logic, retrieval, and decision-making Create feedback and evaluation loops for continual learning Deploy multi-agent collaboration systems that share goals and information dynamically With its focus on clarity and engineering rigor, this book equips you to move beyond static prompts into the era of self-improving, context-driven AI. Whether you're building assistants, copilots, or research agents, DSPy Agentic Systems provides the tools and design thinking to craft intelligent systems that truly evolve over time.

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