Build AI Powered Web Applications

Format: Instructor-Led
Duration: 4 Days • 32 Hours
Instructor: TBD

Overview

In this course, learners gain the practical skills to design and deploy intelligent, data-driven web applications that leverage AI technologies. Through a blend of lectures, demonstrations, and labs, students explore how AI services, APIs, and frameworks can be integrated into full-stack web applications.

Learners will build, train, and deploy AI web components using JavaScript, Next.js, and cloud-based APIs such as OpenAI and Hugging Face. Each week includes lab sessions where participants apply key skills and progressively develop a capstone AI-driven project.

By the end of the course, learners will be able to plan, build, and launch responsive, intelligent web applications aligned with real-world development workflows.

Outcomes

Explain how AI technologies enhance modern web applications.
Configure a local and cloud-based AI development environment.
Develop front-end components using frameworks such as Next.js.
Integrate external AI APIs for text, image, or data generation.
Apply fundamental machine-learning concepts for web development.
Implement pre-trained models and SDKs to add intelligent behaviors.
Design and maintain data pipelines and storage systems for AI applications.
Deploy and present a fully functional AI web application as a capstone project.

Prerequisites

None. This is a beginner level-friendly course with no technical prerequisites. Learners should bring curiosity about how web applications are built and a willingness to experiment with visual design and problem-solving. Comfort with basic computer operations—such as organizing files, navigating browsers, and using online tools—will increase speed in learning new concepts.

System Requirements

Below are details covering the course systems and accounts you need on Day 1. You’ll receive additional information about the rest of the required tools when the course begins. Your instructor may add or remove tools from your course. You will receive a full setup document as part of your course materials.

Technical Requirements: Build AI Web Applications (Online)

Technical requirements - operating system

The following OSs are supported in the Build AI Web Applications course:

Windows: The most recent public release of Windows 11.
macOS: The last two major public releases of macOS.
Ubuntu: The most recent LTS release of Ubuntu.

General Assembly can only offer support for the operating systems listed above. We do not offer support for beta or prerelease versions of any OS. We do not support any mobile/tablet operating systems or ChromeOS.

Students must have an account with administrator privileges on the computer they use for the course to install and configure the applications and tools required.

Technical requirements - hardware

All hardware should meet the minimum requirements to support the software requirements stated above, in addition to the following:

An Intel or AMD dual-core processor with at least a 2.0Ghz base clock speed or any Apple Silicon chip. Note that dual-core processors have a reduced frame rate when screen sharing on Zoom.
8GB of RAM.
Laptops should have at least a 13” screen size.
At least 40GB of free hard disk space.

Additionally, Windows and Ubuntu devices must be capable of enabling Intel Virtualization Technology (VT-x) or AMD Virtualization (AMD-V).

If taking the course in-person, a laptop equipped with Wi-Fi is required at every session.

Additional considerations for Build AI Web Applications

The following are all required at every session if taking the course remotely:

A functioning dual-monitor setup. At least one screen should be 27” or larger.
A functioning webcam (either built-in or external).
Access to stable, fixed broadband internet (100Mbps download / 20 Mbps upload).
A dedicated, quiet, well-lit workspace where you feel comfortable focusing. Preferably, this is a private room.

We highly recommend:

A functioning wired Ethernet connection.
Comfortable headphones.

Build AI Web Applications systems and tools

System used	Needed on day 1?	How to download/access
Canvas	Yes	You will receive access to Canvas after enrollment.
Google Chrome	Yes	Download Chrome here.
Slack	Yes	Download the Slack app here.
Zoom	Yes	Download the Zoom Desktop Client here.
LLM	Yes	It is up to you what LLM you choose. Some options you can consider include: - ChatGPT by OpenAI - Claude by Anthropic - Gemini by GoogleYou only need one LLM to be successful in this course.
GitHub	Yes	Visit https://github.com/ to sign up for a free account.
Visual Studio Code	Yes	Follow the instructions found here to download Visual Studio Code.
Postman / Insomnia	No	Download Postman
PostgreSQL	No	Download PostgreSQL
OpenAI API	Yes	Sign up for OpenAI API access
Node.js	Yes	Access to node.js
Next.js	No	Access to next.js
React	Yes	Access to React
TensorFlow.js	No	Access to TensorFlow.js
LangChain	No	Access to LangChain
Render	No	Get setup with Render

Module Repos

#	Module Name	Module Overview	Associated Labs
1	Introduction to AI-powered Web Applications	Explain the core components and workflows that enable AI integration within web applications.	Analyzing AI App Architecture Analyze an existing AI web app and diagram its data flow and API dependencies.
2	Setting Up the AI Development Environment	Configure an AI-ready web development environment, including API authentication and environment management.	Connecting to the OpenAI API Set up a local environment, connect to the OpenAI API, and test authentication.
3	Advanced Front-end Development With Next.js	Develop AI-responsive front-end components using Next.js for dynamic rendering and data fetching.	Building a Next.js AI Page Build a Next.js page that fetches and displays AI-generated text or images.
4	Connecting to AI and Ml APIs	Integrate AI APIs to send, receive, and render model outputs in a web application.	Creating an AI Middleware Layer Build a middleware layer that connects to an AI API and displays model results.
5	Machine Learning Fundmentals	Describe and apply fundamental ML concepts relevant to integrating AI into web applications.	Running Models with TensorFlow.js Use TensorFlow.js to run a simple in-browser model for image or text classification.
6	Using Pre-trained Models and SDKs	Implement AI functionality using pre-trained SDKs and APIs to enhance web features.	Adding AI Text or Sentiment Features Add a text-generation or sentiment-analysis feature to a web app using a pre-trained model.
7	Data Pipelines for AI Applications	Design and implement simple data pipelines to feed and store AI-related data for web applications.	Building a Data Ingestion Pipeline Build a small data ingestion pipeline to store user input for model improvement.
8	Integrating Headless Cms Platforms	Integrate a headless CMS into a web application for automated, AI-assisted content management.	Serving AI Content with Contentful Configure Contentful to serve AI-generated content dynamically in a web interface.
9	Building AI-driven Personalization Features	Design AI-driven personalization features that tailor user experience based on behavior and preferences.	Creating AI Recommendations with Embeddings Build a recommendation feature that uses embeddings for personalized content suggestions.
10	Implementing Chatbots and Conversational AI	Develop a conversational AI interface that processes user queries and returns contextual responses.	Developing an AI Chatbot Build an AI chatbot that integrates with an external LLM API.
11	A/B Testing and Feature Optimization	Implement A/B testing strategies to evaluate and improve AI-enabled web features.	Running an A/B Test for AI Models Conduct an A/B test comparing two AI recommendation algorithms.
12	Testing and Evaluating AI Applications	Evaluate AI web applications for reliability, bias, and performance through testing and audits.	Testing Model Bias and Accuracy Test model predictions for bias and accuracy using benchmark data.
13	Deploying AI-powered Web Applications	Deploy AI-integrated web applications and monitor real-time API performance and usage.	Deploying an AI Web App Deploy a working AI web app to Render and monitor API calls and latency.
14	Ethical, Secure, and Cost-efficient AI Development	Apply ethical and sustainable practices in developing and deploying AI-powered web solutions.	Auditing API Usage and Ethics Audit API usage, implement rate limiting, and document ethical considerations.
15	Capstone Project: AI-powered Full-stack Web Application	Design, build, and deploy a full-stack AI-powered web application integrating intelligent, ethical, and scalable features.	Building and Presenting a Full AI Application Build and present a deployable AI web application with generative or recommendation capabilities.

📋 Instructional Resources

For teaching notes, preparation steps, and facilitation guidance, see: