Building LLM Applications with Dspy : Replacing Manual Prompts with Systematic Optimization
Language: English
Published by Manning Publications Okt 2026, 2026
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- New
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Neuware - Get the Elektronisches Buch free when you register your print book at Manning.Static, over-engineered prompts quickly lose effectiveness as models and data change. DSPy replaces inflexible text-based prompts with dynamic contract-based Python code, so your prompts can freely adapt and scale. In Building LLM Applications with DSPy, AI engineers Serj Smorodinsky and Brett Kennedy introduce the powerful DSPy framework and show you how it can revolutionize the way you think of prompt and context engineering. In this practical guide, youll learn how DSPy automatically optimizes context, evaluates prompt effectiveness, and automatically tweaks your prompts as models drift and change. As you go, youll get tips and techniques to maintain stable inference results as your apps and agents evolve.Building LLM Applications with DSPy introduces DSPy best practices you can adopt to create reliable, production-ready systems through proper task definition, evaluation, and optimization. Practical to the core, this book helps you construct a full professional portfolio of AI applications, including an LLM-based classification system, a summarizer, and a RAG-based application. You'll build multi-step workflows using DSPy's modular system, finally culminating in fully agentic pipelines, all without writing a single prompt by hand. A DSPy contributor, author Serj Smorodinsky speaks authoritatively about how to get the most out of this elegant tool. And, as with every Manning book, youll find a carefully constructed learning path, readable text, lots of helpful graphics, and our promise that the details are correct and reliable.What's inside Define prompts as Python classes Optimize prompts automatically for higher accuracy Structure complex tasks into simple modulesAbout the readerFor anyone working directly with LLMs, with basic Python skills.About the authorSerj Smorodinsky is a contributor to DSPy, a data scientist, and an AI engineer with over ten years of combined experience in software development and data science. His work spans NLP for customer-service related conversational AI, agentic workflow automation, and LLM evaluation, with hands-on experience leading teams to build chatbots and retrieval-augmented systems for enterprise clients. He also teaches agentic systems and data science in production at Nebius Academy (formerly Y-Data School of Data Science).Brett Kennedy is a data scientist with over thirty years of software development experience and more than ten in data science. He is a regular contributor to open source projects and an author of data science blog articles. He is also the author of Outlier Detection in Python. …
Seller Inventory # 9781633435018
- Title
- Building LLM Applications with Dspy : Replacing Manual Prompts with Systematic Optimization
- Author
- Serj Smorodinsky
- Publisher
- Manning Publications Okt 2026
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 1633435016
- ISBN 13
- 9781633435018
- Item weight
- 417 grams
- Dimensions
- 234x186x26 mm
DSPy (Declarative Self-improving Python), an innovative framework for prompt programming, replaces fragile and unpredictable manual prompts with clean, modular Python code that declares what a model should do, defines the inputs and response formats, and establishes clear evaluation metrics. In Building LLM Applications with DSPy, authors Serj Smorodinsky and Brett Kennedy present a systematic approach for treating prompts as programmatic artifacts rather than loose text strings, so you can build, scale, maintain, and improve complex AI applications with the structural integrity of traditional software.
Practical from page one, this book mirrors a professional AI engineering workflow, taking you from simple classifiers to complex summarizers, advanced RAG, and agentic systems. Reviewer Cyrus Nouroozi, a DSPy contributor, notes that the book “frames the whole subject around a single coherent thesis: prompt programming is a data-driven discipline analogous to machine learning.” To support its strong focus on prompt programming as an engineering process, the book introduces the baseline → evaluate → optimize loop that eliminates the guesswork of traditional prompt design.
Production applications require verifiable, consistent metrics, which are difficult to establish with conventional prompts. This book guides you through constructing custom evaluation metrics, setting up multi-threaded test runners, and calibrating LLM-as-a-judge protocols to gather hard data. Instead of deploying prompts on a whim, you compile and optimize them against structured validation sets. The resulting programs are mathematically proven to be more accurate, more cost-effective, and remarkably resilient to underlying model drift.
The final chapters show you how to build DSPy into fully agentic pipelines, integrating conversation memories, and connecting agents to external environments via MCP. This focused 9-chapter book also introduces cutting-edge DSPy v3 features like SIMBA and GEPA optimizers to squeeze maximum performance out of smaller, cheaper open-weight language models. It is an indispensable resource for any modern programmer wanting to build robust, self-improving, and production-ready generative AI systems.
How to
- Build modular LLM applications using declarative Python signatures
• Automate prompt optimization using SIMBA and GEPA
• Construct rigorous, repeatable metric functions to verify outputs
• Deploy reliable, multi-hop RAG systems and agents
About the reader
This book is designed for software developers, data scientists, and AI engineers with basic Python skills.
About the authors
Serj Smorodinsky is a DSPy contributor, data scientist, and AI engineer with over ten years of experience leading teams to build conversational AI, conversational chatbots, and agentic workflows for enterprise clients. Brett Kennedy is a data scientist with over thirty years of software development experience. He is a regular contributor to open-source projects and the author of Outlier Detection in Python.
"Synopsis" may belong to another edition of this title.
About the Author
Serj Smorodinsky is a DSPy contributor, data scientist, and AI engineer with over ten years of experience leading teams to build conversational AI, conversational chatbots, and agentic workflows for enterprise clients. Brett Kennedy is a data scientist with over thirty years of software development experience. He is a regular contributor to open-source projects and the author of Outlier Detection in Python.
"About the title" may belong to another edition of this title.
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