Author Description

DSPy Crash Course 2026 A Fast-Track Guide to Agentic AI, Declarative LLM Workflows, RAG Optimization, Multimodal Pipelines, Self-Improving Agents, and Production-Ready Python AI Applications Modern AI systems are no longer built with prompts alone. They are engineered. DSPy Crash Course 2026 is a practical, fast-track guide for developers, AI engineers, and data scientists who want to move beyond prompt engineering and build structured, agentic, and self-improving AI systems using the DSPy framework. This book teaches you how to design declarative LLM workflows, construct agentic AI architectures, and deploy production-ready Python applications that scale reliably in real-world environments. Every concept is grounded in engineering principles, real implementations, and practical design patterns used by modern AI teams. Inside this crash course, you will learn how to: Build agentic AI systems with predictable behavior and modular design Use DSPy's declarative programming model to replace fragile prompt chains Design and optimize Retrieval-Augmented Generation (RAG) pipelines Implement self-improving agents using evaluation-driven optimization Create multimodal AI pipelines that combine text, tools, and structured data Integrate DSPy with Python, LangChain, and LangGraph for real applications Debug, evaluate, and refine complex AI workflows with confidence Deploy production-ready AI systems built for performance and reliability Unlike fragmented tutorials or surface-level introductions, this book provides a clear, structured path from fundamentals to advanced implementation. Each chapter builds on the last, combining theory, architecture, and full working code examples to ensure deep understanding and practical m