Building A Simple Chatbot With Openai Structured Outputs

Emily Johnson

-Mar 12, 2026, 1:20 PM

building a simple chatbot with openai structured outputs

In August 2024, OpenAI introduced "Structured Outputs", a remarkable new capability in the Chat Completions API and Assistants API, and successor to the earlier "JSON mode." As the name suggests, Structured Outputs enables developers to obtain consistent, predictable JSON outputs from OpenAI chatbots, according to a predefined schema. Essentially, it's just a reliable way to get ChatGPT to output JSON. In this guide, we'll explore how to leverage Structured Outputs by building a simple chatbot capable of providing detailed flight information in response to a simple user query. We'll then use Helicone to monitor our chatbot's performance. Structured Outputs allow you to generate structured data from unstructured inputs to your chatbots.

Unlike unstructured text, structured outputs are organized, making them easier to parse, manipulate, and integrate into various applications. The key difference between the new Structured Outputs and the likely soon-to-be-deprecated JSON mode lies in its enhanced reliability. OpenAI strongly recommends transitioning to Structured Outputs for any functionality previously handled by JSON mode. JSON is one of the most widely used formats in the world for applications to exchange data. Structured Outputs is a feature that ensures the model will always generate responses that adhere to your supplied JSON Schema, so you don’t need to worry about the model omitting a required key, or... Some benefits of Structured Outputs include:

In addition to supporting JSON Schema in the REST API, the OpenAI SDKs for Python and JavaScript also make it easy to define object schemas using Pydantic and Zod respectively. Below, you can see how to extract information from unstructured text that conforms to a schema defined in code. Structured Outputs is available in our latest large language models, starting with GPT-4o. Older models like gpt-4-turbo and earlier may use JSON mode instead. Structured Outputs is an OpenAI API feature that ensures responses and tool calls adhere to a defined JSON schema. This makes building with our models more reliable, bridging the gap between unpredictable model outputs and deterministic workflows.

This repository contains a collection of sample apps showcasing the use of Structured Outputs. Each app demonstrates practical ways to leverage this feature to build applications with NextJS. Explore the code, update it, and use it in your own projects or as a starting point! There are three sample apps in this repository: This article contains affiliate links. I may earn a commission at no extra cost to you.

title: "Build Your First AI Chatbot in 30 Minutes: Complete Beginner Tutorial with OpenAI API" published: true description: "Learn to create a functional AI chatbot with conversation memory, error handling, and deploy it for... Ever wanted to build your own AI chatbot but felt overwhelmed by the complexity? You're in the right place! In this tutorial, we'll create a fully functional chatbot from scratch using Node.js and OpenAI's API. By the end, you'll have a working chatbot with conversation memory that you can deploy for free. First, let's get your OpenAI API credentials:

Important: Never commit API keys to version control. We'll use environment variables to keep them safe. OpenAI's models like GPT-4 and GPT-3.5 have made it easier to create natural, context-aware chatbots without building your own language models or training pipelines. Whether you're developing an assistant for customer queries or building a knowledge interface, OpenAI’s API gives you the core functionality required to handle conversational AI logic with minimal infrastructure. This guide explains how to build a simple chatbot using OpenAI’s Python SDK. You’ll learn how to structure the conversation, manage memory, and optionally include function calling.

All code is modular and can be extended into production deployments. A chatbot built on OpenAI models is an application that sends user messages to a large language model and returns its responses in real time. These models process input text, understand context, and generate answers or suggestions. With newer versions like GPT-4o, the models support both single-turn and multi-turn interactions, along with external tool usage. To get started, make sure the following are ready: Create a Python file (e.g., simple_chatbot.py).

Import the OpenAI client and load the API key: Artificial Intelligence (AI) and chatbots are transforming the way we interact with technology. From customer service to personal assistants, AI-powered chatbots offer efficient, scalable, and intelligent solutions. In this article, we’ll guide you through creating a simple yet powerful chatbot using OpenAI’s GPT model, LangChain for prompt management, and Streamlit for a user-friendly interface. This guide is perfect for those looking to explore AI chatbot development with minimal resources and straightforward steps. For those interested in exploring open-source alternatives, check out my article on creating a chatbot with Ollama, which provides a lightweight solution using local LLMs.

The chatbot we’ll build will serve as a helpful assistant capable of answering user queries on various topics. By leveraging OpenAI’s gpt-3.5-turbo model (or GPT-4 for advanced use cases), LangChain’s chain management, and Streamlit’s UI components, you can create an engaging and dynamic user experience. First, sign up for an OpenAI account if you don’t already have one. Navigate to the API section, create a new API key, and save it. A step-by-step guide to building intelligent conversational AI using OpenAI's powerful API. Perfect for beginners and experienced developers alike.

Master chatbot development with practical examples and deployable code. Our comprehensive tutorial covers everything you need to create sophisticated AI chatbots that understand context, provide meaningful responses, and integrate seamlessly into your applications. Learn to leverage GPT-4 and other cutting-edge models to create chatbots with human-like conversation abilities and contextual understanding. Follow along with real, production-ready code samples in Python and JavaScript that you can adapt and deploy to your own projects immediately. Deploy your chatbot to popular platforms like websites, Slack, Discord, or custom applications with our detailed deployment guides and best practices. This step-by-step guide covers function calling, response formatting and monitoring with Helicone.

Building A Simple Chatbot With Openai Structured Outputs

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