Learn the fundamentals of DSPy and how to use its signature and module-based programming model to build modular, traceable, and debuggable GenAI agentic applications.
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Instructor: Chen Qian
Learn the fundamentals of DSPy and how to use its signature and module-based programming model to build modular, traceable, and debuggable GenAI agentic applications.
Build agentic applications by chaining DSPy modules like Predict, ChainOfThought, and ReAct, and use MLflow to trace and debug your programs.
Optimize your GenAI apps with DSPy Optimizer by automating prompt tuning and improving few-shot examples to improve answer accuracy and consistency.
Join DSPy: Build and Optimize Agentic Apps, built in partnership with Databricks and taught by Chen Qian, a software engineer at Databricks and co-lead maintainer of the DSPy framework.
Agentic AI applications tackle complex tasks such as document automation, question-answering, and multi-step decision-making. However, building these applications can become complex and one challenge is writing and maintaining good prompts. DSPy is a flexible open-source framework that simplifies your applicationâs interaction with LLMs. It streamlines your workflow by utilizing modular blocks in which you can provide a dataset of inputs and desired output, and systematically build, trace, and optimize your application.
This course teaches you how to use DSPy to build and optimize LLM-powered applications. Youâll write programs using DSPyâs signature-based programming model, debug them with MLflow tracing, and automatically improve their accuracy with DSPy Optimizer. Along the way, youâll see how DSPy helps you easily switch models, manage complexity, and build agents that are both powerful and easy to maintain.
Youâll immediately put the concepts to work, first by coding a sentiment classifier in roughly 30 lines, then stretching the same pattern into a âName the Celebrityâ guessing game. Next, youâll trace every step of a travel-booking assistant with MLflow, and wrap up by letting DSPy Optimizer lift a Wikipedia-based RAG agentâs accuracy, all without hand-tuning prompts.
By the end of this course, youâll be able to build structured, robust, and adaptable GenAI applications with DSPy, ready to run on whichever LLM comes next.
This course is ideal for anyone who wants a more reliable, maintainable way to build and debug multi-step agents. No prior DSPy experience or knowledge is required.
Introduction
Introduction to DSPy
DSPy Programming - Signatures and Modules
Debug Your DSPy Agent with MLflow Tracing
Optimizing Agents with DSPy Optimizer
Conclusion
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