Quick Guide & Tips

đŸ’»   Accessing Utils File and Helper Functions

In each notebook on the top menu:

1:   Click on "File"

2:   Then, click on "Open"

You will be able to see all the notebook files for the lesson, including any helper functions used in the notebook on the left sidebar. See the following image for the steps above.

đŸ’»   Downloading Notebooks

In each notebook on the top menu:

1:   Click on "File"

2:   Then, click on "Download as"

3:   Then, click on "Notebook (.ipynb)"

đŸ’»   Uploading Your Files

After following the steps shown in the previous section ("File" => "Open"), then click on "Upload" button to upload your files.

📗   See Your Progress

Once you enroll in this course—or any other short course on the DeepLearning.AI platform—and open it, you can click on 'My Learning' at the top right corner of the desktop view. There, you will be able to see all the short courses you have enrolled in and your progress in each one.

Additionally, your progress in each short course is displayed at the bottom-left corner of the learning page for each course (desktop view).

đŸ“±   Features to Use

🎞   Adjust Video Speed: Click on the gear icon (⚙) on the video and then from the Speed option, choose your desired video speed.

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√   Hide and Unhide Lesson Navigation Menu: If you do not have a large screen, you may click on the small hamburger icon beside the title of the course to hide the left-side navigation menu. You can then unhide it by clicking on the same icon again.

🧑   Efficient Learning Tips

The following tips can help you have an efficient learning experience with this short course and other courses.

🧑   Create a Dedicated Study Space: Establish a quiet, organized workspace free from distractions. A dedicated learning environment can significantly improve concentration and overall learning efficiency.

📅   Develop a Consistent Learning Schedule: Consistency is key to learning. Set out specific times in your day for study and make it a routine. Consistent study times help build a habit and improve information retention.

Tip: Set a recurring event and reminder in your calendar, with clear action items, to get regular notifications about your study plans and goals.

☕   Take Regular Breaks: Include short breaks in your study sessions. The Pomodoro Technique, which involves studying for 25 minutes followed by a 5-minute break, can be particularly effective.

💬   Engage with the Community: Participate in forums, discussions, and group activities. Engaging with peers can provide additional insights, create a sense of community, and make learning more enjoyable.

✍   Practice Active Learning: Don't just read or run notebooks or watch the material. Engage actively by taking notes, summarizing what you learn, teaching the concept to someone else, or applying the knowledge in your practical projects.

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Welcome back!

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We'd like to know you better so we can create more relevant courses. What do you do for work?

DeepLearning.AI
Short CourseIntermediate1 hour 11 mins

Getting Structured LLM Output

Instructors: Will Kurt, Cameron Pfiffer

DotTxt logo
  • Intermediate
  • 1 hour 11 mins
  • 7 Video Lessons
  • 4 Code Examples
  • Instructors: Will Kurt, Cameron Pfiffer
    • DotTxt
    DotTxt

What you'll learn

  • Get an overview of structured output generation, its importance, and the different approaches to generating them.

  • Build a social media agent using structured output and learn how to use re-prompting libraries like instructor.

  • Understand the concepts behind constrained decoding and how the LLM logits are modified to get a particular output structure.

About this course

Welcome to Getting Structured LLM Output, built in partnership with DotTxt, and taught by Will Kurt, Founding Engineer, and Cameron Pfiffer, Developer Relations Engineer at DotTxt.

When building production-ready software, it’s challenging to parse through and rely on freeform text outputs. Structured outputs—like JSON—solve this by converting natural language into consistent, clear, and programmable data that a machine can read and process.

In this course, you’ll learn how to generate structured outputs while building several use cases, including a social media analysis agent.

You’ll gain a fundamental understanding of structured outputs and learn efficient ways to generate outputs in your defined schema or format. You’ll begin by using structured output APIs, then follow it up by utilizing re-prompting libraries like “instructor” to generate structured output. Afterward, you’ll learn how constrained decoding works, in which constraints are applied on each subsequent token generated, blocking any tokens that don’t fit your defined schema.

In detail, you’ll:

  • Learn why structured outputs are important, how they allow for scalable software development, and the different approaches to generate them, including vendor-provided APIs, re-prompting libraries, and structured generation.
  • Build a simple social media agent using OpenAI’s structured output API, learn how to define a model’s desired structured output using Pydantic, and perform basic programming with your outputs, such as importing structured data into a data frame using pandas.
  • Learn how to use the open-source library “instructor,” which checks the structured output of the model and re-prompts the model until it validates the desired output, and explore the limitations of this approach.
  • Understand how structured generation by “outlines” works by modifying LLM logits, per token generated based on instruction, to give a particular output structure.
  • Learn how regular expressions, which power outlines, are represented as finite-state machine, and how they can be used to develop a range of structured output beyond JSON.

By the end of this course, you’ll have broadened your knowledge of the approaches you can use to get structured outputs from your LLM applications.

Who should join?

It’s helpful to be familiar with Python, the basics of LLM prompting, and LLM application development.

Course Outline

7 Lessons・4 Code Examples

  • Introduction

    Video・3 mins

  • Introduction to Structured Output Generation

    Video・13 mins

  • How To Use Structured Outputs

    Video with Code Example・11 mins

  • Retry-based Structured Output

    Video with Code Example・11 mins

  • Structured Generation with Outlines

    Video with Code Example・13 mins

  • Structured Generation: Beyond JSON

    Video with Code Example・16 mins

  • Conclusion

    Video・1 min

  • Quiz

    Graded・Quiz

    ・10 mins

Instructors

Will Kurt

Will Kurt

Cameron Pfiffer

Cameron Pfiffer

Developer Relations Engineer at DotTxt

Course access is free for a limited time during the DeepLearning.AI learning platform beta!

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