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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Building LLM Applications with Dspy: Replacing Manual Prompts with Systematic Optimization

Building LLM Applications with Dspy: Replacing Manual Prompts with Systematic Optimization

Smorodinsky, Serj
Kennedy, Brett

Paperback

General ComputersProgramming

PREORDER - Expected ship date October 27, 2026

ISBN10: 1633435016
ISBN13: 9781633435018
Publisher: Manning Publications
Published: Oct 20 2026
Pages: 302
Weight: 0.66
Language: English
Get the eBook 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, you'll learn how DSPy automatically optimizes context, evaluates prompt effectiveness, and automatically tweaks your prompts as models drift and change. As you go, you'll 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, you'll 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

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