| title | An Interactive AI Tutorial With Concepts and Lessons Learned |
|---|---|
| description | Don't waste time with incomplete examples and local environment issues. Just learn by doing. |
| mode | wide |
| slug | index |
In our work and as university teachers, we wanted real code — not notebooks — without the setup friction. We wanted to go beyond "Hello World" examples and understand the production implications: cost, reliability, security, and scale. We needed something our students could open and start experimenting with immediately — no install, no API key drama. And yes, we wanted all of this free and open source.
So we built this: every example runs in your browser, every pattern is production-tested, and every concept is explained with code you can modify and break.
Every example runs in an embedded sandbox right in your browser via StackBlitz.
- No installation required — start coding in seconds
- Multi-provider — OpenAI, Gemini, and Claude — switch with one env variable
- Instant feedback — modify code, run it, see results
- Fork and experiment — break things safely, learn by doing
This isn't another "Hello World" tutorial. We cover the advanced techniques that make AI systems production-ready:
- Prompt caching strategies that significantly reduce costs
- Hybrid search and reranking for RAG systems that return relevant results
- MCP-based agent architecture with multi-server tool discovery
- Evaluation frameworks that catch issues before production
- Security guardrails for PII detection, jailbreak prevention, and output filtering
- Tool design patterns that improve agent accuracy from 58% to 92%
AI is learned by doing, not reading. Every module is designed around hands-on learning.
Begin your journey with prompt design fundamentals. Learn structured prompting patterns that reduce hallucinations and lower costs. Master structured prompts, advanced techniques, and cost optimization. Foundation for everything else. Build RAG systems with hybrid search, reranking, and evaluation. Production patterns for connecting LLMs to your data. Build agents with MCP servers, thread-based memory, business rule validation, and security guardrails. Multi-Agent Systems, Fine-Tuning, and Evaluation — new modules in development.Start with Context Engineering & Prompt Design to build your foundation.