This demo showcases a chatbot that integrates with a ‘main menu’ structured output schema. The user can interact with the chatbot to navigate through four primary functions:

• Apply for a Job
• Order Catering
• Book a Private Party
• Reserve a Table

Key Features:

1. Main Menu Navigation:
   The chatbot allows users to access different services seamlessly through a structured menu. It ensures smooth navigation with a clear, easy-to-follow flow.
2. Interruptible Conversations:
   At any point during the interaction, the chatbot can interrupt the conversation to ask a question and get a response from the RAG (retrieval-augmented generation) assistant. This ensures the user gets the right information without waiting for a linear conversation to finish.
3. Welcome Message:
   This demo marks the first time I’ve used a welcome message. It’s designed to greet users and immediately guide them through the available options, enhancing the first interaction and setting the tone for a smooth experience.

Conclusion:

This demo highlights the power of a well-structured chatbot in providing real-time interaction and assistance to restaurant customers, improving both user experience and operational efficiency.

On August 6, 2024, OpenAI introduced something new called structured outputs for their API.

Normal software can’t easily take a long piece of text and find specific information in it. For example, let’s say we have a paragraph like this:

“Elena Marquez is a 34-year-old urban planner from San Diego. She grew up appreciating diverse cultures and earned a degree in Environmental Design. Now, she focuses on transforming neglected urban spaces into community hubs.”

If we wanted to use this paragraph to make a profile of Elena Marquez, a normal computer program wouldn’t know which part of the paragraph tells us her name or her age. It doesn’t understand the meaning of the words like we do.

But if the information was put into a format like this, the computer could understand it better:

{ “fullName”: “Elena Marquez”, “age”: 34, “profession”: “Urban Planner”, “location”: “San Diego”, “background”: “Grew up appreciating diverse cultures and earned a degree in Environmental Design.”, “careerFocus”: “Transforms neglected urban spaces into community hubs.” }

In this format, each piece of information has a label. For example, “fullName” is labeled as “Elena Marquez,” and “age” is labeled as 34. Now, the computer can easily pick out Elena’s name, age, job, and other details.

This helps us make a complete profile of Elena that can be stored in a database. We could use that profile to show information about Elena on a website or to find just one part of her profile, like her age or job.

So, structured outputs allow us to take any text and turn it into a format that software can understand and use.

We can also use this idea with conversations. For example, if a person talks to an AI chatbot, we can use structured outputs to take the information from the conversation and put it into a format that can be added to a database automatically.

This is helpful because sometimes we want information to be easy for humans to read (like a paragraph), and other times we want it to be organized so that a software can easily work with it. Structured outputs let us change between these formats so we can use the information in different ways.