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
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