Overview

Discover the definitive guide to mastering DSPy, the revolutionary framework that replaces fragile prompt engineering with declarative, self-optimizing AI programs. In Engineering DSPy: The Practitioner's Guide to Compiling Robust, Self-Optimizing AI Agents, you'll learn how to build reliable, production-grade LLM applications-from advanced RAG systems and multi-tool agents to self-correcting pipelines-without endless manual prompt tweaking. Written for software engineers, ML practitioners, and AI architects, this comprehensive book takes you from foundational signatures and composable modules to advanced optimization techniques like MIPRO and COPRO, assertions for safety guardrails, model distillation for cost efficiency, and full operational deployment. Key topics include: Overcoming prompt brittleness with programmatic compilation Designing retrieval-augmented generation (RAG) that learns signal from noise Creating agentic workflows with ReAct, tools, and hallucination reduction Enforcing constraints with assertions and backtracking Distilling GPT-4-level performance into smaller, quantized models Tracing, caching, versioning, and FastAPI deployment Complete with a hands-on capstone project building a self-healing technical support agent, plus migration guidance from LangChain, common pitfalls, and a glossary. Move beyond prompt engineering-start programming language models that improve themselves. Ideal for anyone building scalable AI agents with DSPy.

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