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.
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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.
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:
OpenAI offers a variety of models via an API that you can use to build powerful AI-enhanced products.
GPT-4: Powerful Large Language Model suitable for building AI chat experiences, language translation systems, and text generation applications.
DALL·E: Image Generation Model suitable for creating generative AI art.
Whisper: Advanced Speech Recognition Model suitable for developing voice-controlled AI applications and virtual assistants.
Huggingface
Huggingface offers a variety of powerful AI models such as Mistral's Mistral-7B and Meta's Llama-2-7b. These cover use cases like computer vision, natural language processing, etc.
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.
Pi's chat interface
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.
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.
OpenAI
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:
