Programmatically Developing LLM Prompts With DSPy

How can you move from manually writing prompts for an LLM application toward defining them programmatically? This week on the show, Brett Kennedy returns to discuss his new book “Building LLM Applications with DSPy.” With large language models becoming embedded in production software, manual prompt engineering becomes brittle, time-consuming, and difficult to maintain. Brett shares how DSPy replaces manually writing and adjusting prompts with structured prompt programming. We dig into how DSPy uses declarative signatures to define inputs and outputs. We also discuss how developers programmatically compile, tune, and optimize prompts for specific LLM-based tasks within an application. Course Spotlight: Using LlamaIndex for RAG in Python Learn how to set up LlamaIndex, load your data, build and persist an index, and run queries to get grounded answers with RAG in Python. Topics: 00:00:00 – Introduction 00:01:50 – Working on a new book 00:02:53 – Writing with a partner 00:04:16 – Building LLM Applications with DSPy 00:05:22 – What is DSPy? 00:06:58 – Working with signatures and typing 00:09:53 – Prompt engineering failing to work for a new model 00:13:43 – What is an optimizer? 00:19:04 – Moving toward a new level of abstraction in prompting 00:23:19 – Prompts for the LLM within a consumer facing application 00:28:26 – Video Course Spotlight 00:30:21 – Example of using with RAG 00:38:45 – Example of a business plan tool 00:43:06 – The Bitter Lesson 00:46:03 – Structure of the book 00:49:55 – Advice on starting with DSPy 00:54:15 – Example of a transformed prompt 00:59:37 – What are you excited about in the world of Python? 01:01:47 – What do you want to learn next? 01:04:11 – What’s the best way to follow your work online? 01:04:40 – Thanks and goodbye Show Links: Building LLM Applications with DSPy - Serj Smorodinsky and Brett Kennedy DSPy DSPy: The framework for programming—not prompting—language models - GitHub Tutorials Overview - DSPy GEPA optimization - DSPy Let the LLM Write the Prompts: An Intro to DSPy in Compound AI Pipelines - YouTube Episode #208: Detecting Outliers in Your Data With Python Tabular Models Benchmark: Performance Across 19 Datasets 2026 Brett Kennedy on Manning Serj Smorodinsky on Manning W Brett Kennedy – Medium Brett Kennedy - LinkedIn Level up your Python skills with our expert-led courses: Building Type-Safe LLM Agents With Pydantic AI Accessing Multiple AI Models With the OpenRouter API Using LlamaIndex for RAG in Python Support the podcast & join our community of Pythonistas