Build AI Apps with OpenAI, LangChain & React

Build production-ready AI apps with OpenAI, LangChain, React, RAG, AI Agents, CI/CD, and real-world projects.

Created bySkillBakery Studio

Last updated 6/2026

English

What you'll learn

Understand how Large Language Models (LLMs) and modern AI applications work.
Master Prompt Engineering techniques for generating reliable AI responses.
Integrate OpenAI APIs into real-world applications.
Build AI-powered applications using LangChain and React.
Create streaming chat applications with real-time AI responses.
Develop AI Agents capable of tool calling and multi-step reasoning.
Build Retrieval-Augmented Generation (RAG) applications.
Protect AI systems against prompt injection and security risks.
Optimize AI applications for performance, scalability, and cost.
Monitor AI quality using logging, analytics, and feedback loops.
Implement model fallback strategies and prompt A/B testing.
Build production-ready AI APIs using modern software architecture.
Automate testing and deployment using GitHub Actions and CI/CD.
Deploy and maintain AI applications following industry best practices.

Course content

11 sections • 94 lectures • 7h 59m total length

Introduction – Traditional vs Future Web Apps4:57
LLM Capabilities4:42
LLM Context5:13
AI Blueprint4:58
LLM Demo App9:29
Common AI App Architecture Patterns3:31

Streaming Responses4:13
Streaming Responses – Demo3:17
Explore how to implement streaming in a React and OpenAI setup using server sent events, including endpoints, stream service, and chunked responses demonstrated in Postman.
React Streaming Architecture3:39
React Streaming – Demo5:49
Background Jobs for AI Tasks5:13
Background Jobs – Demo6:34
Hardening API Security5:05
Hardening API Security – Demo6:35
Redis Caching – Theory5:00
Redis Caching – Demo3:28
Redis Server Integration Demo5:41
Observability & Logging4:09
Observability & Logging – Demo6:24

AI-First UX Patterns6:00
AI-First UX Patterns – Demo3:26
Streaming UI – Theory4:37
Master the architecture of real-time AI streaming UI and implement incremental delivery with tokens. Explain how server-sent events and front-end streams replace dead air with a responsive, transparent conversational rhythm.
Streaming UI – Demo3:48
Graceful Failure Handling4:13
Graceful Failure – Demo4:54
Architecting User Intent4:36
User Intent – Demo5:18
Architecting Next-Generation AI Interfaces3:41
Design modular, predictable AI workflows by starting with structured forms and finishing with chat refinement, treating AI as a headless engine rather than a chat box.
Next-Generation AI Interfaces – Demo4:15
AI Continuity & Conversation Flow4:30
AI Continuity – Demo5:44

AI Guardrails & Security Overview5:02
Guardrails – Demo4:43
Advanced Prompt Injection Defense4:39
Prompt Injection Defense – Demo4:06
Output Guardrails & Moderation4:44
Output Moderation – Demo3:11
Demonstrates output moderation using a moderation service to check safety, sanitize unsafe results, and block harmful content before it reaches users.
Building a Production-Grade AI Backend6:09
Production-Grade AI Backend – Demo5:10

Reducing Token Usage4:43
Token Optimization – Demo3:33
Async & Parallel Chains4:48
Learn to eliminate sequential bottlenecks by converting to parallel chains using promise.all, and blend sequential and parallel steps for fast, reliable AI applications.
Async & Parallel Chains – Demo3:21
Accelerate AI backends using async and parallel execution to run getExplanation, getKeyPoints, and getExample in parallel. Leverage agents for decision making and parallel AI for content generation.
Scaling AI APIs Horizontally5:30
Scale AI APIs by decoupling user requests from model execution using an async queue blueprint with a fast-track API, Redis, and background workers to handle infinite growth.
Cost Monitoring & Alerts4:44
Cost Monitoring & Alerts – Demo5:36

Environment & Secret Management5:25
Environment & Secret Management – Demo3:31
CI/CD Pipeline for AI Applications5:00
Monitoring AI Quality Over Time5:36
Handling Model Changes & Deprecations4:32
Prompt A/B Testing4:44
Production Readiness Demo (End-to-End AI Career Coach)10:08
GitHub Actions & Automated Deployment6:26
Automate code validation and deployment for a production-ready AI career coach app using GitHub Actions, mastering CI-CD workflows, tests, secrets, and automatic deployments.

Requirements

Basic knowledge of JavaScript is recommended.
Familiarity with React will be helpful but not mandatory.
Basic understanding of APIs and HTTP requests.
A computer running Windows, macOS, or Linux.
Node.js and Visual Studio Code installed.
An OpenAI API key (instructions provided in the course).
A willingness to learn by building real-world AI applications.
No prior experience with LangChain, OpenAI APIs, or AI development is required.

Description

AI Engineering with OpenAI, LangChain & React

Artificial Intelligence is transforming the way modern applications are built. This course is designed to take you beyond simple chatbot examples and teach you how to build production-ready AI applications using today's most powerful technologies.

Whether you're a software developer, full-stack engineer, or AI enthusiast, this hands-on course will guide you through every stage of AI application development—from understanding Large Language Models (LLMs) to deploying scalable AI systems in production.

Throughout the course, you'll build real-world projects while learning how to integrate OpenAI, LangChain, and React to create intelligent applications with streaming responses, AI agents, tool calling, Retrieval-Augmented Generation (RAG), security, monitoring, performance optimization, and deployment best practices.

What you'll build

AI-powered chat applications
Streaming AI interfaces
AI agents capable of using external tools
Retrieval-Augmented Generation (RAG) applications
Production-ready AI APIs
React-based AI frontends
AI monitoring and analytics dashboards
Secure and scalable AI services

Topics Covered

Prompt Engineering
OpenAI API Integration
LangChain Fundamentals
Streaming AI Responses
React Chat Applications
Function Calling & Tool Calling
AI Agents & ReAct Pattern
Retrieval-Augmented Generation (RAG)
Prompt Injection Protection
AI Security & Guardrails
Token Optimization
Performance & Cost Monitoring
Async & Parallel Processing
AI Observability
Model Fallback Strategies
Prompt A/B Testing
GitHub Actions & CI/CD
Production Deployment Best Practices

By the end of this course, you'll have the skills and confidence to design, develop, deploy, monitor, and maintain AI-powered applications using modern engineering practices.

If your goal is to become an AI Engineer capable of building production-ready applications—not just prototypes—this course is for you.

Who this course is for:

Software Developers looking to add AI skills to their toolkit.
Frontend and React Developers who want to build AI-powered user interfaces.
Backend Developers integrating AI into web applications.
Full-Stack Developers building intelligent applications.
Engineers transitioning into AI Engineering roles.
Students interested in Generative AI and Large Language Models.
Technical Architects exploring AI application design.
Anyone who wants to build production-ready AI applications using modern tools and best practices.

Build AI Apps with OpenAI, LangChain & React

What you'll learn

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Course content

Foundations6 lectures • 33min

OpenAI API Deep Dive8 lectures • 47min

LangChain Core Concepts12 lectures • 1hr 5min

Retrieval-Augmented Generation12 lectures • 1hr 1min

Backend Architecture for AI Apps13 lectures • 1hr 5min

React Frontend for AI Experiences12 lectures • 55min

Agents & Tool Calling7 lectures • 36min

Security, Safety & Guardrails8 lectures • 38min

Performance, Cost & Scaling7 lectures • 32min

Deployment & Production Readiness8 lectures • 45min

Requirements

Description

Who this course is for: