React Server Components in AI Applications

In this workshop, we discuss the merits of serverless architecture and how it can be applied to the AI space. We'll explore options around building serverless RAG applications for a more lambda-esque approach to AI. Next, we'll get hands on and build a sample CRUD app that allows you to store information and query it using an LLM with Workers AI, Vectorize, D1, and Cloudflare Workers.

Knowledge of AI tooling is critical for future-proofing the careers of React developers, and the Vercel suite of AI tools is an approachable on-ramp. In this course, we’ll take a closer look at the Vercel AI SDK and how this can help React developers build streaming interfaces with JavaScript and Next.js. We’ll also incorporate additional 3rd party APIs to build and deploy a music visualization app.
Topics:- Creating a React Project with Next.js- Choosing a LLM- Customizing Streaming Interfaces- Building Routes- Creating and Generating Components - Using Hooks (useChat, useCompletion, useActions, etc)

Today every developer is using LLMs in different forms and shapes, from ChatGPT to code assistants like GitHub CoPilot. Following this, lots of products have introduced embedded AI capabilities, and in this workshop we will make LLMs understandable for web developers. And we'll get into coding your own AI-driven application. No prior experience in working with LLMs or machine learning is needed. Instead, we'll use web technologies such as JavaScript, React which you already know and love while also learning about some new libraries like OpenAI, Transformers.js

Join Nathan in this hands-on session where you will first learn at a high level what large language models (LLMs) are and how they work. Then dive into an interactive coding exercise where you will implement LLM functionality into a basic example application. During this exercise you will get a feel for key skills for working with LLMs in your own applications such as prompt engineering and exposure to OpenAI's API.
After this session you will have insights around what LLMs are and how they can practically be used to improve your own applications.
Table of contents: - Interactive demo implementing basic LLM powered features in a demo app- Discuss how to decide where to leverage LLMs in a product- Lessons learned around integrating with OpenAI / overview of OpenAI API- Best practices for prompt engineering- Common challenges specific to React (state management :D / good UX practices)

In this workshop we'll take a tour of applied AI from the perspective of front end developers, zooming in on the emerging best practices when it comes to working with LLMs to build great products. This workshop is based on learnings from working with the OpenAI API from its debut last November to build out a working MVP which became PowerModeAI (A customer facing ideation and slide creation tool).
In the workshop they'll be a mix of presentation and hands on exercises to cover topics including:
- GPT fundamentals- Pitfalls of LLMs- Prompt engineering best practices and techniques- Using the playground effectively- Installing and configuring the OpenAI SDK- Approaches to working with the API and prompt management- Implementing the API to build an AI powered customer facing application- Fine tuning and embeddings- Emerging best practice on LLMOps

Today every developer is using LLMs in different forms and shapes. Lots of products have introduced embedded AI capabilities, and in this workshop you’ll learn how to build your own AI application. No experience in building LLMs or machine learning is needed. Instead, we’ll use web technologies such as JavaScript, React and GraphQL which you already know and love.