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AI Engineering for Web Developers

Build real AI-powered features using LLM APIs, embeddings, RAG, and agents. A hands-on course for web developers ready to add AI to their toolkit, with a build project in every module.

Intermediate54 hours10 modules55 lessons

Curriculum

01LLM Fundamentals

255min
  • Free PreviewWhat Are Large Language Models?
    45min
  • Transformers Explained for Web Developers
    60min
  • Tokens, Context Windows, and Pricing
    45min
  • The Model Landscape: Choosing the Right LLM
    45min
  • ProjectModule Project: Model Showdown
    75min

02Prompt Engineering

275min
  • Message Roles: System, User, and Assistant
    50min
  • Few-Shot Prompting: Teaching by Example
    50min
  • Chain of Thought Prompting
    50min
  • Structured Output: Getting JSON from LLMs
    75min
  • ProjectModule Project: Prompt Lab
    75min

03OpenAI API Deep Dive

315min
  • Chat Completions API
    60min
  • Streaming Responses
    60min
  • Function Calling & Tool Use
    60min
  • Cost Management
    60min
  • ProjectModule Project: AI Code Reviewer
    75min

04Anthropic API & Claude

315min
  • Anthropic Messages API
    60min
  • Claude Tool Use
    60min
  • Working with Long Context
    60min
  • Claude Best Practices
    60min
  • ProjectModule Project: Doc Analyzer
    75min

05Building AI Features in Web Apps

335min
  • Architecture Patterns for AI Web Apps
    65min
  • Building a Chat UI
    65min
  • Streaming AI Responses to the Browser
    65min
  • Error Handling & Resilience for AI Features
    65min
  • ProjectModule Project: AI Chat Widget
    75min

06Embeddings & Vector Databases

315min
  • What Are Embeddings?
    60min
  • Generating Embeddings with the OpenAI API
    60min
  • Vector Databases: pgvector and Pinecone
    60min
  • Building Semantic Search End-to-End
    60min
  • ProjectModule Project: Smart Bookmark Search
    75min

07RAG from Scratch

385min
  • What Is RAG and Why It Matters
    65min
  • Document Chunking Strategies
    65min
  • Building a Retrieval Pipeline
    65min
  • Evaluating RAG Systems
    65min
  • RAG with LlamaIndex and LangChain
    50min
  • ProjectModule Project: Ask My Docs
    120min

08AI Agents

375min
  • What Are AI Agents?
    60min
  • The ReAct Pattern
    60min
  • Giving Agents Tools
    60min
  • Multi-Step Planning, Memory, and Guardrails
    60min
  • Agent Frameworks: LangGraph and CrewAI
    60min
  • ProjectModule Project: Research Agent
    120min

09Full-Stack AI App (Capstone)

375min
  • Capstone Project Setup: AI Knowledge Base App
    75min
  • Building the Document Ingestion Pipeline
    75min
  • Building the Chat Interface with RAG
    75min
  • Polish and Ship: Auth, History, Citations, and Deployment
    75min
  • ProjectModule Project: AI Knowledge Base (Capstone)
    75min

10Deployment & Production

440min
  • Managing LLM Costs in Production
    75min
  • Caching Strategies for AI Applications
    75min
  • Safety Guardrails for AI Applications
    75min
  • Monitoring and Observability for AI Applications
    75min
  • Scaling and Best Practices for AI Features
    75min
  • Prompt Evaluation with Langfuse
    60min
  • Durable AI Workflows with Temporal
    45min
  • ProjectModule Project: Production Hardening
    75min

Prerequisites

  • JavaScript (ES6+)
  • Basic Python
  • Familiarity with REST APIs
  • HTML/CSS fundamentals
$49

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DifficultyIntermediate
Duration54 hours
Modules10
Lessons55