OpenAI is an AI research lab that has developed a number of tools for researchers and developers including natural language processing, robotics, and reinforcement learning. By leveraging the OpenAI APIs, we can build faster and smarter, while bringing new and interesting capabilities to help enhance existing solutions and solve new challenges.

Let's get started building with AI using the [OpenAI SDK](https://platform.openai.com/) by installing and configuring the SDK in a Node.js project. Here you'll learn how to install the [openai npm package](https://www.npmjs.com/package/openai) into an existing project, import the openai dependency, and configure the SDK with your OpenAI API Key to make authenticated requests to the OpenAI API.

[https://platform.openai.com/overview](https://platform.openai.com/overview)
https://platform.openai.com/overview

Install and Configure the OpenAI SDK in a Node.js Project

Image generation provides us a way to bring life to natural language through a visual medium. Through a simple or detailed description, we can generate a completely unique image that tells the story we want whether that's for dynamically creating blog images or exploring new forms of art.

We can use AI to generate images with DALL-E and the OpenAI SDK in Node.js. In this lesson you'll learn how to use the [createImage](https://platform.openai.com/docs/api-reference/images/create) method generate an image using a descriptive prompt, the number of images you want to generate, and the size of the image. 

[https://platform.openai.com/docs/api-reference/images/create](https://platform.openai.com/docs/api-reference/images/create)


Generate Images with AI in Node.js Using the OpenAI SDK & DALL-E

The more descriptive an image prompt, the better the results will be. That includes being able to take user input to help articulate what the image should look like.

Dynamic prompts in Next.js serverless functions can help us generate images based off of application input with the OpenAI SDK. You'll learn how to configure an API Endpoint to parse a body of a POST request to grab a dynamic prompt value and use it to configure the prompt for the createImage method.

[https://nextjs.org/docs/api-routes/introduction](https://nextjs.org/docs/api-routes/introduction)

Create Dynamic Image Generation Prompts for the OpenAI SDK in Next.js API Endpoints

ChatGPT and similar conversational AI tools have quickly made impressions in the tech industry and beyond by giving the people using those tools a more natural and a potentially more helpful means to get answers to the questions they have. Bringing GPT into applications can unlock new ways to solve problems and quickly help customers to solve theirs.

We can create ChatGPT-like conversational messages with the OpenAI SDK in a Node.js project. You'll learn how to use the [createChatCompletion](https://platform.openai.com/docs/api-reference/chat/create) method and configure the gpt-3.5-turbo model to generate a response based on a message prompt.

[https://platform.openai.com/docs/api-reference/chat/create](https://platform.openai.com/docs/api-reference/chat/create)


Generate Chat Completions with AI in Node.js Using the OpenAI SDK & GPT

Requests that we typically make in an application come all at the same time. We make the request, we get a response. But using a streaming mechanism, we can get our information as it comes resulting in a quicker initial response, as well as set ourselves up for different user experiences including conversational-like messaging.

Using Edge Functions in Next.js, we can stream our response back to the application and create an experience similar to the official ChatGPT interface. You'll learn how to set up a new Edge function, make a request directly to the OpenAI API, and listen for the stream events in Next.js with [eventsource-parser](https://www.npmjs.com/package/eventsource-parser).

- [https://vercel.com/docs/concepts/functions/edge-functions/edge-functions-api](https://vercel.com/docs/concepts/functions/edge-functions/edge-functions-api)

Stream OpenAI Chat Completions to a Next.js App Using Vercel Edge Functions

Getting a long string as a response makes a lot of sense if you're building an app or tool that appears conversational or if you just want to display a little bit of information as a response, but if you want to do something programmatic with the information, such as using different datapoints from the information in different parts of the UI, you'll have a hard time parsing out that information from a single request.

To get around this, we can simply ask GPT to return our data formatted as JSON! Further, we can ask it in a particular shape. You'll learn how to format a chat completion prompt to request JSON data and gain confidence in that data by defining the shape in the request.

Requesting JSON Chat Completions from OpenAI for Programmatic Use


An important part in creating a data API whether based on generative systems or not is being able to reliably generate responses in the format you need. Variable data structures and irrelevant information could break your application or frustrate the users of your API, resulting in downtime or lost customers.

Using the messaging API, we can train GPT to become more aware of our needs by using System roles to instruct GPT on behavior and User to Assistant examples to give GPT precedent to base its next response on.

[https://platform.openai.com/docs/guides/chat/introduction](https://platform.openai.com/docs/guides/chat/introduction)

Training GPT Using Roles & Message Examples for Better Chat Completions

With a reliable data API, we can start using our generative output in creative ways, including feeding that directly into a new generation like creating an image based off of a blog post.

We'll look at taking a GPT-generated blog post and feeding it into DALL-E along with some stylistic prompt engineering to generate an image for our post.

Use Chat Completions for Image Generation Prompts with DALL-E

Using the models that OpenAI provides will get you really far, but their knowledge base can be somewhat limited for particular information or if its past a certain date that the model doesn't support.

To improve the experience, we can train models to have a better understanding of the data we want to work with, whether that's documentation for a new developer tool or guiding the model to respond closer to how how we expect.

The problem—is you need a lot of data in a specific format to do this and its not very practical to write it all our by hand. So we can instead use GPT, where by providing our data, we can ask it to generate JSONL training data for us and format it in a way that we can use to fine tune our model.


- [https://platform.openai.com/docs/guides/fine-tuning/prepare-training-data](https://platform.openai.com/docs/guides/fine-tuning/prepare-training-data)

Generating & Preparing JSONL Training Data with GPT for Fine Tuning an OpenAI Model

Once we have our training data, we need to a way to deliver it to OpenAI.

We can do that by uploading our training data file to OpenAI directly where we can then reference that with our fine tuning.


- [https://platform.openai.com/docs/api-reference/files/upload](https://platform.openai.com/docs/api-reference/files/upload)


Uploading Training Data Files to OpenAI for Fine Tuning AI Models

We can teach an AI model to better understand the information we want to generate responses from. To do that, we can use training data in a particular format, JSONL.

Once we have our data generated and uploaded to OpenAI, we can now use it to fine tune or customize a model to provide better results.

We'll use the training data we generated and uploaded in previous lessons to create a new fine-tuned model based off of the OpenAI davinci model.


- [https://platform.openai.com/docs/api-reference/fine-tunes/create](https://platform.openai.com/docs/api-reference/fine-tunes/create)

Get Started with AI-Driven App Development Using the OpenAI Node.js SDK

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