Leveraging LLMs to Build Intuitive AI Experiences With JavaScript

certificate
Recording and certification are available to Multipass and Full ticket holders only
Please login if you have one.
Rate this content
Bookmark

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

This workshop has been presented at JSNation 2024, check out the latest edition of this JavaScript Conference.

FAQ

The presenters are a Developer Relations Engineer from Couchbase and Roy, who works at IBM.

The workshop discusses technologies such as LLMs, generative AI, VectorSearch, TensorFlow.js, Transformers.js, and various JavaScript libraries.

Vector databases store vector embeddings of data, which can be used for semantic search and retrieval augmented generation (RAG) to make AI models context-aware.

AI applications can be made production-ready using local models by deploying them in containerized environments like Docker or Kubernetes, or on virtual machines, ensuring privacy and control over data.

Recommended models for generating JavaScript code include Code Llama, Dolphin Mistral, and Phi-3, which are specialized and lightweight for code generation tasks.

The live workshop focuses on leveraging large language models (LLMs) and generative AI with the help of JavaScript for front-end development.

Yes, TensorFlow.js can run AI models directly in the browser, offering benefits like lower latency, higher privacy, and reduced server costs.

OLAMA is a tool that allows you to run open-source large language models (LLMs) locally. It supports models like LLAMA3, Dolphin Mistral, and others, which can be served locally for offline use.

Using local models offers better privacy since data isn't sent to external servers, reduced latency, and potentially lower costs by avoiding cloud service fees.

Few-Shot Prompting involves providing an LLM with a few examples of questions and their desired formats to guide the model in generating more accurate responses.

Roy Derks
Roy Derks
Shivay Lamba
Shivay Lamba
108 min
07 Jun, 2024

Comments

Sign in or register to post your comment.
Video Summary and Transcription
The workshop explores leveraging LLMs and generative AI with JavaScript, focusing on incorporating AI in JavaScript, leveraging TensorFlow.js for ML, and using AI models and backend technologies. It covers using TensorFlow.js toxicity model, Transformers JS with WebWorkers, and building AI applications with existing models. The workshop also delves into prompt engineering for zero-shot prompts and implementing few-shot prompting. It concludes with exploring fuchsia prompts and embeddings, prompt engineering, and context awareness, and offline usage with TensorFlow embeddings.
Video transcription and chapters available for users with access.

Watch more workshops on topic

AI on Demand: Serverless AI
DevOps.js Conf 2024DevOps.js Conf 2024
163 min
AI on Demand: Serverless AI
Top Content
Featured WorkshopFree
Nathan Disidore
Nathan Disidore
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.
AI for React Developers
React Advanced 2024React Advanced 2024
142 min
AI for React Developers
Featured Workshop
Eve Porcello
Eve Porcello
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)
Llms Workshop: What They Are and How to Leverage Them
React Summit 2024React Summit 2024
66 min
Llms Workshop: What They Are and How to Leverage Them
Featured Workshop
Nathan Marrs
Haris Rozajac
2 authors
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)
Working With OpenAI and Prompt Engineering for React Developers
React Advanced 2023React Advanced 2023
98 min
Working With OpenAI and Prompt Engineering for React Developers
Top Content
Workshop
Richard Moss
Richard Moss
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
Building AI Applications for the Web
React Day Berlin 2023React Day Berlin 2023
98 min
Building AI Applications for the Web
Workshop
Roy Derks
Roy Derks
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.
Building Your Generative AI Application
React Summit 2024React Summit 2024
82 min
Building Your Generative AI Application
WorkshopFree
Dieter Flick
Dieter Flick
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

Check out more articles and videos

We constantly think of articles and videos that might spark Git people interest / skill us up or help building a stellar career

Building a Voice-Enabled AI Assistant With Javascript
JSNation 2023JSNation 2023
21 min
Building a Voice-Enabled AI Assistant With Javascript
Top Content
This Talk discusses building a voice-activated AI assistant using web APIs and JavaScript. It covers using the Web Speech API for speech recognition and the speech synthesis API for text to speech. The speaker demonstrates how to communicate with the Open AI API and handle the response. The Talk also explores enabling speech recognition and addressing the user. The speaker concludes by mentioning the possibility of creating a product out of the project and using Tauri for native desktop-like experiences.
AI and Web Development: Hype or Reality
JSNation 2023JSNation 2023
24 min
AI and Web Development: Hype or Reality
Top Content
This talk explores the use of AI in web development, including tools like GitHub Copilot and Fig for CLI commands. AI can generate boilerplate code, provide context-aware solutions, and generate dummy data. It can also assist with CSS selectors and regexes, and be integrated into applications. AI is used to enhance the podcast experience by transcribing episodes and providing JSON data. The talk also discusses formatting AI output, crafting requests, and analyzing embeddings for similarity.
The Rise of the AI Engineer
React Summit US 2023React Summit US 2023
30 min
The Rise of the AI Engineer
Watch video: The Rise of the AI Engineer
The rise of AI engineers is driven by the demand for AI and the emergence of ML research and engineering organizations. Start-ups are leveraging AI through APIs, resulting in a time-to-market advantage. The future of AI engineering holds promising results, with a focus on AI UX and the role of AI agents. Equity in AI and the central problems of AI engineering require collective efforts to address. The day-to-day life of an AI engineer involves working on products or infrastructure and dealing with specialties and tools specific to the field.
TensorFlow.js 101: ML in the Browser and Beyond
ML conf EU 2020ML conf EU 2020
41 min
TensorFlow.js 101: ML in the Browser and Beyond
TensorFlow.js enables machine learning in the browser and beyond, with features like face mesh, body segmentation, and pose estimation. It offers JavaScript prototyping and transfer learning capabilities, as well as the ability to recognize custom objects using the Image Project feature. TensorFlow.js can be used with Cloud AutoML for training custom vision models and provides performance benefits in both JavaScript and Python development. It offers interactivity, reach, scale, and performance, and encourages community engagement and collaboration between the JavaScript and machine learning communities.
Web Apps of the Future With Web AI
JSNation 2024JSNation 2024
32 min
Web Apps of the Future With Web AI
Web AI in JavaScript allows for running machine learning models client-side in a web browser, offering advantages such as privacy, offline capabilities, low latency, and cost savings. Various AI models can be used for tasks like background blur, text toxicity detection, 3D data extraction, face mesh recognition, hand tracking, pose detection, and body segmentation. JavaScript libraries like MediaPipe LLM inference API and Visual Blocks facilitate the use of AI models. Web AI is in its early stages but has the potential to revolutionize web experiences and improve accessibility.
The Ai-Assisted Developer Workflow: Build Faster and Smarter Today
JSNation US 2024JSNation US 2024
31 min
The Ai-Assisted Developer Workflow: Build Faster and Smarter Today
AI is transforming software engineering by using agents to help with coding. Agents can autonomously complete tasks and make decisions based on data. Collaborative AI and automation are opening new possibilities in code generation. Bolt is a powerful tool for troubleshooting, bug fixing, and authentication. Code generation tools like Copilot and Cursor provide support for selecting models and codebase awareness. Cline is a useful extension for website inspection and testing. Guidelines for coding with agents include defining requirements, choosing the right model, and frequent testing. Clear and concise instructions are crucial in AI-generated code. Experienced engineers are still necessary in understanding architecture and problem-solving. Energy consumption insights and sustainability are discussed in the Talk.