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Practice AI engineering where you already code (JavaScript)

Quick answer: Scrimba stacks prompting, RAG, agents, MCP, and deployable AI UIs into interactive scrims—so you edit prompts and tool calls in-browser instead of watching another LLM hype reel. Start with the AI Engineer Path when you can ship a basic web app.

Last reviewed: March 2026.

Who this is for​

Web developers who can call an API and want hireable AI features—not a second PhD in statistics.

AI engineering here means shipping products with models, not notebook-only experiments—and Scrimba’s catalog is built for that loop.

Why Practice AI Engineering on Scrimba?​

  1. Build real AI apps — not just prompting exercises, but full applications with APIs, databases, and user interfaces
  2. Interactive format — pause and modify AI code to see how different prompts, models, and architectures behave
  3. 15+ AI courses — the largest interactive AI engineering curriculum, covering RAG, agents, MCP, and more
  4. No ML degree required — if you know JavaScript, you can start building AI applications today

Top AI Engineering Courses on Scrimba​

AI Engineer Path​

Duration: 11.4 hrs | Level: Intermediate | Access: Pro

The structured path covering everything from prompt engineering to building production AI agents. Includes multiple hands-on projects.

Learn AI Agents​

Level: Intermediate | Access: Pro

Build autonomous AI agents that can use tools, make decisions, and complete multi-step tasks. Covers agent loops, tool calling, and memory.

Learn RAG (Retrieval-Augmented Generation)​

Level: Intermediate | Access: Pro

Build AI systems that query your own data. Learn embeddings, vector databases, chunking strategies, and how to ground LLM responses in facts.

Intro to Model Context Protocol (MCP)​

Access: Free

Learn Anthropic's open standard for connecting AI models to external tools and data sources. Build MCP servers and clients.

Intro to AI Engineering​

Level: Intermediate | Access: Pro

Covers the basics of working with LLM APIs, prompt design, and building your first AI-powered app. Check Scrimba for current availability.

Build AI Apps with Cloudflare​

Access: Pro

Deploy AI applications to the edge using Cloudflare Workers AI. Learn to build and deploy production AI apps.

AI Engineering Practice Projects​

Project 1: Build a Chatbot with Memory​

Use the OpenAI API to build a chatbot that remembers conversation history. Practice: API calls, message arrays, system prompts, and streaming responses.

Project 2: Build a RAG System​

Create a system that answers questions about your own documents. Practice: text chunking, embedding generation, vector similarity search, and prompt augmentation.

Project 3: Build an AI Agent with Tools​

Create an agent that can search the web, read files, and execute code. Practice: tool definitions, agent loops, and error handling.

Project 4: Build an MCP Server​

Create a Model Context Protocol server that exposes your data to AI assistants. Practice: protocol implementation, tool definitions, and resource exposure.

How to Practice AI Engineering Effectively​

  1. Start with APIs — learn to call OpenAI, Anthropic, or Mistral APIs before building complex systems
  2. Build incrementally — start with a basic chatbot, add memory, then add tools, then add RAG
  3. Test with real data — use your own documents, not toy datasets
  4. Deploy something — put an AI app online so you can show it to employers or clients

Choose This If​

Choose this guide if: You want course recommendations for hands-on practice. Most require Pro.

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