How to build an AI app: A step-by-step guide

With the rise of technologies like OpenAI's GPT-4 and Replicate's cloud-hosted models, building an artificial intelligence (AI) app has become easier than ever.

You don't need to have a PhD in machine learning or knowledge of how to set up Kubernetes clusters/Docker containers to deploy production-scale AI applications. Instead, you can leverage these tools and platforms to create your own AI applications.

Examples of AI apps

There are several types of AI apps that you can build:

Chatbots
Writing tools
Generative AI apps
Agents / Virtual assistants

We've seen a wave of exciting new AI companies built on Vercel, leveraging tools like Next.js and the Vercel AI SDK:

This guide will walk you through the process of building an AI app using the latest technologies. Topics covered will include everything from data collection to model training and deployment. You will craft a performant, beautiful AI app without extensive technical expertise.

Step 1: Identifying the problem

Before you dive into building your AI app, ask yourself:

What specific issue or task will your AI app address?
Who is your target audience, and what are their pain points?
How can AI technology effectively solve this problem or enhance the user experience?

Identifying the problem is the foundational step that will guide the rest of your development process.

Step 2: Choosing an AI model

Selecting the right model is crucial for the success of your app. Depending on your use case, you can choose the appropriate pre-trained models from the following providers:

Step 3: Building the frontend

Once you have the right model, you'll need to build the frontend for your AI application.

Your frontend is the first impression for your users. Much like how It's important that you use an appropriate model, it's important that you make a good first impression.

Take popular AI products like ChatGPT and Pi for instance – they both have a fast and intuitive user experience.

Here's a good tech stack for building your first AI application:

Next.js: is a React framework that gives you building blocks to create web applications. Not only is it used by some of the largest companies in the world, it is actually what both ChatGPT and Pi are built on as well.
AI SDK: an open-source library designed to help developers build conversational streaming user interfaces in JavaScript and TypeScript.
TailwindCSS: a utility-first CSS framework that makes it easy to rapidly build your UI designs.
Vercel: a cloud platform that provides serverless deployment and hosting for your AI app. It offers seamless integration with Next.js and makes it easy to deploy and scale your application.

To help you get started, we built a Next.js AI Chatbot template that uses this stack to create a chat experience with edge streaming.

Step 4: Fine-tuning your model

Off-the-shelf models can get you a solid MVP. To stand out from your competition, you can go the extra mile by fine-tuning your model.

A fine-tuned model means you won't need to provide as much context to get better model performance. This can save on token usage and allow for faster response times when prompting.

We've written a guide on how to fine-tune LLMs like GPT-3.5 for your reference. You can also fine-tune text-to-image diffusion models using tools like Dreambooth as well.

Step 5 (optional): Monetize your app

You may decide that you want to redeem some of your AI costs. To do this, you could implement monetization via a subscription model, usage-based pricing, etc.

Here are some templates that you can refer to when building out monetization for your app:

Summary

AI apps are becoming more prevalent. It's valuable to understand how AI technology works and how you can build your own.

This guide gives you an overview of how you can get started building your own AI applications today.

We're excited to see what you build!