Prompt Engineering Toolkit

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)

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.

Generative AI is exciting tech enthusiasts and businesses with its vast potential. In this session, we will introduce Retrieval Augmented Generation (RAG), a framework that provides context to Large Language Models (LLMs) without retraining them. We will guide you step-by-step in building your own RAG app, culminating in a fully functional chatbot.
Key Concepts: Generative AI, Retrieval Augmented Generation
Technologies: OpenAI, LangChain, AstraDB Vector Store, Streamlit, Langflow

GenAI agents are one of the most promising directions for complex GenAI based applications. These agents can search the web, code, and carry complex tasks completely autonomously for the user. In this workshop we will learn the basics of GenAI agents. Define the basic terms and frameworks and understand how they differ from traditional use of LLMs.We will understand prompting techniques for LLM agents and specifically the React prompting technique for AI agents (not to be confused with React the programming language). We will build, from scratch, our own GenAI agent that can interact with the user, perform web searches and code and execute in Javascript.Table of contents:- Introduction to GenAI agents- Understanding the React framework- Hands-on Building of simple GenAI agent- Deployment of the Agent with streamlit- Tips, and frameworks for developing GenAI agents