Congratulations!

1. Congratulations!

You did it! Congratulations! Let's recap everything you've learned.

2. Chapter 1: Welcome to the Responses API

In Chapter 1, you got up-to-speed with one of the best tools in an AI developer's toolkit: the OpenAI Responses API. You practiced making requests, adjusting reasoning, token counts, and models to optimize for cost, quality and latency, and enabled back-and-forth conversations.

3. Chapter 2: Web Search

In Chapter 2, you saw the Responses API's native web search functionality - no sign in, and no extra API keys required. This allowed you to overcome the model's knowledge limitations and pull up-to-date information from the internet.

4. Chapter 2: Function-Calling Tools

For more customized solutions, you learned how to turn any Python function into a tool for your LLM. This was a five step process that allowed the model to choose tools, executing those tools with the arguments the LLM chose, and returning the output to the model so it can answer the original question.

5. Chapter 3: Structured Outputs

In Chapter 3, you learned about powerful patterns that enable production applications, starting with structured outputs. You used the pydantic library to define classes that enabled structured and reliable outputs.

6. Chapter 3: Streaming and Semantic Events

You learned how to stream outputs back to the user, rather than making them wait until the full output is generated. With this, you learned about semantic events, where we can design custom logic or triggers when particular events occur. This means you can design reactive and informative interfaces that keep users in the loop.

7. Chapter 3: Multimodality

Finally, we sent image and text inputs to answer questions about images, and classify images into categories.

8. Let's practice!

Congratulations! Best of luck on the remainder of your AI developer journey!

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This exercise is part of the course

Working with the OpenAI Responses API

IntermediateSkill Level

5.0+

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Building AI applications has never been easier with the Responses API! Learn how to prompt models using a simple interface, best practices for working with reasoning models, and how to build back-and-forth conversations with LLMs.

Exercise 1: The Responses API: a Developer's Best Friend Exercise 2: Your First Responses API Call Exercise 3: Adding Model Instructions Exercise 4: Extracting Information from the Response Exercise 5: Mastering Response Parameters Exercise 6: Experimenting with More Powerful Models Exercise 7: Reasoning About Reasoning Exercise 8: Conversation Chaining Exercise 9: From One Message to Another Exercise 10: Enabling Chat Conversations

LLMs are powerful, but they're even more powerful once tools are introduced! Learn how to integrate web search with your LLM systems with no additional API keys or credentials. Discover that tools can be, well... pretty much anything! Build your own tools for LLMs to call to go beyond the model's base capabilities.

Exercise 1: Response Items: The Building Blocks Exercise 2: Extracting Output Items and Their Contents Exercise 3: Using Items for Custom Handling Exercise 4: Web Search That Just Works Exercise 5: Combining LLMs and Web Search Exercise 6: Building Confidence with LLM Sources Exercise 7: Function-Calling Tools: Defining the Function Exercise 8: The Function-Calling Tool Workflow Exercise 9: Defining a Function for Converting Timezones Exercise 10: Function-Calling Tools: The Full Workflow Exercise 11: Building the OpenAI Tool Definition Exercise 12: Integrating Function-Calling Tools

Move your LLM systems from proof-of-concept to production with three key design paradigms. Learn how to generate consistent and reliable structured outputs, like JSON, using LLMs. You'll also use semantic events to stream updates to your users, creating interactive and enjoyable experiences. Finally, go multi-modal and learn how to combine text and images with OpenAI's GPT-5 models.

Exercise 1: Generating Structured Outputs Exercise 2: Consistent Outputs, Every Time!Exercise 3: Nesting Pydantic Classes Exercise 4: Streaming with Semantic Events Exercise 5: Streaming OpenAI Responses Exercise 6: Streaming Semantic Events Exercise 7: Streaming with Function Calls Exercise 8: Input Flexibility and Multimodality Exercise 9: Role-Based Inputs Exercise 10: Prompting with Images from URLs Exercise 11: Prompting with Local Image Files Exercise 12: Congratulations!

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