Hands-On Context Engineering with DSPy (Paperback)
Roberto Pizzlo
Sold by Grand Eagle Retail, Bensenville, IL, U.S.A.
AbeBooks Seller since October 12, 2005
New - Soft cover
Condition: New
Ships within U.S.A.
Quantity: 1 available
Add to basketSold by Grand Eagle Retail, Bensenville, IL, U.S.A.
AbeBooks Seller since October 12, 2005
Condition: New
Quantity: 1 available
Add to basketPaperback. Master the Future of AI Systems with DSPy and Context EngineeringIn today's fast-moving world of large language models, context engineering is the key to building reliable, scalable, and intelligent applications. This hands-on guide shows you how to unlock the full potential of DSPy, the framework designed to simplify, optimize, and automate AI pipelines.Whether you're a developer, data scientist, or AI enthusiast, this book takes you step by step through building self-improving pipelines, tuning prompts and parameters automatically, integrating with Langfuse and MLflow, and deploying at scale in cloud environments. Every concept is paired with practical, executable examples-so you don't just learn the theory, you apply it immediately to real-world scenarios like retrieval-augmented generation (RAG).What sets this book apart is its clear, professional style and its focus on production-ready systems. By the end, you'll know how to monitor, improve, and maintain DSPy applications with confidence-turning prototypes into robust, enterprise-grade solutions.Why this book?Comprehensive yet practical: Covers everything from DSPy basics to advanced optimization loops.Future-proof skills: Learn how to scale across multiple models, providers, and production environments.Author credibility: Written by Roberto Pizzlo, a technology author known for distilling complex systems into clear, actionable guides.This isn't just another AI book-it's your hands-on companion for mastering the art of context engineering and building intelligent systems that get better with time.Inside the Book (Table of Contents Highlights)Chapter 1: Foundations of Context Engineering with DSPyChapter 4: Building Retrieval-Augmented Generation PipelinesChapter 6: Ensuring Reliability with Assertions and EvaluationChapter 8: Experiment Tracking with Langfuse and MLflowChapter 9: Building Self-Improving Pipelines with DSPy CompilersChapter 10: Deploying and Maintaining DSPy Applications in ProductionAppendices: API Quick Reference, Troubleshooting, Tools, and Glossary This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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