Uploaded April 2025 | Updated September 2026, 2 weeks ago
Learn more: bit.ly/3FLpRcN
We’ve teamed up with DotTxt to bring you a new short course: Getting Structured LLM Output, taught by Will Kurt (Founding Engineer) and Cameron Pfiffer (Developer Relations Engineer).
When you're building with LLMs, one challenge becomes clear quickly: the outputs are unpredictable. The same prompt might return different formats, structures, or levels of detail—none of which are easy for downstream systems to work with.
If you're building production-ready software, this unpredictability becomes a bottleneck.
In this course, you’ll learn how to make LLMs output data in consistent, structured formats like JSON—turning natural language into programmable data your software can reliably use.
You’ll explore several practical techniques:
- Using structured output APIs, such as OpenAI’s, to generate well-formed responses from the start
- Re-prompting with libraries like Instructor, which validate outputs and retry until they fit your schema
- Constraining token generation using Outlines, an open-source library that enforces structure at the token level with regular expressions
You’ll also apply what you learn in a hands-on project: a social media analysis agent that processes user posts, identifies sentiment, and outputs structured data that can drive automated workflows.
Whether you’re using open-source models or vendor APIs, this course gives you practical tools and a clearer understanding of how to bring more reliability to your LLM applications.
Enroll now: bit.ly/3FLpRcN
Learn more: bit.ly/3FLpRcN
We’ve teamed up with DotTxt to bring you a new short course: Getting Structured LLM Output, taught by Will Kurt (Founding Engineer) and Cameron Pfiffer (Developer Relations Engineer).
When you're building with LLMs, one challenge becomes clear quickly: the outputs are unpredictable. The same prompt might return different formats, structures, or levels of detail—none of which are easy for downstream systems to work with.
If you're building production-ready software, this unpredictability becomes a bottleneck.
In this course, you’ll learn how to make LLMs output data in consistent, structured formats like JSON—turning natural language into programmable data your software can reliably use.
You’ll explore several practical techniques:
- Using structured output APIs, such as OpenAI’s, to generate well-formed responses from the start
- Re-prompting with libraries like Instructor, which validate outputs and retry until they fit your schema
- Constraining token generation using Outlines, an open-source library that enforces structure at the token level with regular expressions
You’ll also apply what you learn in a hands-on project: a social media analysis agent that processes user posts, identifies sentiment, and outputs structured data that can drive automated workflows.
Whether you’re using open-source models or vendor APIs, this course gives you practical tools and a clearer understanding of how to bring more reliability to your LLM applications.
Enroll now: bit.ly/3FLpRcN
