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Practice AI Engineering

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.

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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