Prompt engineering gets messy fast. What starts as a simple instruction can turn into endless tweaking, context adjustments, and rework when model behavior changes. @DSPyOSS approaches that differently by treating LLM apps more like software you can optimize instead of prompts you endlessly babysit.

Building LLM Applications with DSPy shows how to use DSPy’s contract-based Python framework to optimize context, evaluate prompt effectiveness, and adapt as models drift.

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Building LLM Applications with DSPy - Serj Smorodinsky and Brett Kennedy

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Look insideStatic, 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. InBuilding LLM Applications with DSPy, AI engineersSerj SmorodinskyandBrett Kennedyintroduce 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 DSPyintroduces 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 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, authorSerj Smorodinskyspeaks 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
• Optimize prompts automatically for higher accuracy
• Structure complex tasks into simple modules

about the reader

For anyone working directly with LLMs, with basic Python skills.

about the authors

Serj Smorodinskyis a contributor to DSPy, a data scientist, and 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 Kennedyis 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 ofOutlier Detection in Python.

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