Build AI-Powered Apps with OpenAI and Node.js

Scott Moss

Scott Moss

Superfilter AI
3 hours, 34 minutes CC
Build AI-Powered Apps with OpenAI and Node.js

Course Description

Learn to integrate OpenAI's powerful language models into your Node.js apps. Set up Node.js with the OpenAI API, build an AI chat interface, and then get hands-on with image generation, semantic search, and document QA systems. Dive into practical projects like creating semantic searches for movies and developing QA applications with YouTube and PDF data. Explore AI-driven function calling and learn to build real-world AI-powered Node.js applications!

This course and others like it are available as part of our Frontend Masters video subscription.


Course Details

Published: November 27, 2023

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Table of Contents


Section Duration: 12 minutes
  • Introduction
    Scott Moss introduces and provides an overview of the course notes and code repository.
  • Course Overview
    Scott talks about the current state of AI and provides an overview of the course content, including building a chat experience, semantic search, document QA, and function calling. The requirements for the course are Node.js and having an OpenAI API key. The course will mainly focus on using OpenAI but will discuss on other models and services.


Section Duration: 54 minutes
  • Intro to LLMs
    Scott introduces Large Language Models (LLMs) and explains what they are and how they work. The role of these models in the AR world and the use cases for LLMs, such as writing, content creation, customer support, research, and education, are also discussed.
  • Setup Node & OpenAI API
    Scott sets up the project and installs the necessary dependencies. They then explain how to set up the API key using a .env file and demonstrate how to import and use the OpenAI library. They show how to make a chat request to the AI and log the response. The instructor also discusses the concept of tokens and how they affect the cost and response of the AI. They demonstrate how to send messages to the AI and receive responses, and explain the limitations of chat models in terms of memory and non-determinism. Finally, they introduce the concept of a chatbot and explain the difference between the code they have written and a chatbot.
  • Simple AI Chat in Node.js
    Scott creates a Node command-line chat interface using the OpenAI GPT-3 model. The readline module reads input from the command line, and a recursive function continuously prompts the user for input and generates responses from the AI model.
  • Scaling Chat & AI Temperature
    Scott explains the scaling issues that come with chat-based applications. Token limits, memory constraints, and the need for creative solutions to handle these limitations are discussed.

Search & Langchain

Section Duration: 52 minutes
  • Search & Langchain Overview
    Scott introduces semantic search, which goes beyond simple string matching to understand the meaning and context of words. Scott shares some examples of semantic search in action, such as autocomplete suggestions and recommendation systems. Langchain is introduced, and the semantic search feature in the documentation is demonstrated.
  • Embeddings & Vectors
    Scott introduces word embeddings and vectors and describes how embeddings are collections of vectors that represent the meaning behind words. Embeddings are useful for storing and comparing the meanings of words and are essential for semantic search.
  • Creating a Semantic Search
    Scott provides an example of how semantic search can be used to find movies with similar themes. A movie recommendation semantic search application is created, and the initial required libraries are imported. A createStore method is written to return an in-memory vector store from a list of movie documents.
  • Performing a Similarity Search
    Scott codes the search function, which converts a query into an embedding and uses cosine similarity to compare it with other embeddings. The score of each search result is displayed demonstrating the proximity of each result.
  • Scaling Semantic Search
    Scott discusses the challenges and considerations when deploying and scaling semantic search. Challenges include handling large volumes of data, updating and maintaining indexes and embeddings, ensuring query accuracy, optimizing for speed and accuracy, and using caching and feedback loops.

Question Answering Systems

Section Duration: 42 minutes

Function Calling

Section Duration: 41 minutes

Wrapping Up

Section Duration: 11 minutes
  • Wrapping Up
    Scott wraps up the course by providing additional recommendations and suggestions to further explore OpenAI APIs. Topics including diffusion models and training models on GPUs are discussed. Students are encouraged to continue building on the knowledge gained in the course and to experiment with different ideas and applications.
I just completed "Build AI-Powered Apps with OpenAI and Node.js" by Scott Moss on Frontend Masters! Thank you Scott, your courses are great! This course is very exciting, because you are an amazing instructor!
Margaret M Mosall

Margaret M Mosall


This recent LLM AI App Development course by Scott Moss on Frontend Masters is amazing! Always love his courses on there. This one was especially practical. no frills, advice on scaling and common pitfalls that you will face in the real-world. Cheers!



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