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.
There are several types of AI apps that you can build:
- Writing tools
- Generative AI apps
- Agents / Virtual assistants
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.
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.
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:
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.
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.
- 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.
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.
Early tests have shown a fine-tuned version of GPT-3.5 Turbo can match, or even outperform, base GPT-4-level capabilities on certain narrow tasks.
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.
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:
Metered Billing Stripe Starter
Metered billing for an AI content creator app, powered by Tier, OpenAI, and Vercel.
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!
Here are some additional resources for you learn more about building AI applications:
- Building ChatGPT from Scratch by Andrej Karpathy
- OpenAI Cookbook
- Prompt Engineering Guide by Brex
- LLM University
- The State of GPT
- What is ChatGPT doing and why does it work
- How is LlamaCPP Possible?
- On Prompt Engineering
- Transformers from Scratch
- Anti-hype LLM reading list
- Numbers every LLM Developer should know
- Get a GPU machine for pushing models