CSE58 · GenAI, RAG, MCP & agents
RAG Retrieval Ranking Playground
Ask questions about the fictional college handbook. Tune chunk size, keyword vs meaning search and top-k, and read the citations.
🎯 You will learn to
- Chunk documents
- Compare keyword, meaning and hybrid search
- Cite or abstain
🧪 Live environment
Runs entirely in your browser: nothing is installed, no real cloud account is used and your code is never sent to our server.
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✅ Steps & checkpoints
🗺️ Retrieval tuning
Chunk size, search type and top-k decide which handbook passages reach the answer.
The 2D diagram shows the exact flow, ports and trust boundaries. The 3D scene is a concept view of the same parts; packets travel along the same arrows.
🤔 Think about it
Why should the bot refuse when no chunk is relevant?
🛡️ Safety & cost
The agents are deterministic simulations: no model is called and no real tool is executed. Write tools always need human approval.
🧹 Cleanup
Nothing to clean up.
📚 Reference documents
- Retrieval-augmented generation in Azure AI Search · Microsoft Learn
- Azure AI Search · Microsoft Learn
- Model Context Protocol · MCP
- OWASP Top 10 for LLM applications · OWASP
- Agentic AI Lens (Well-Architected) · AWS
- Microsoft Foundry Agent Service · Microsoft
Official sources, checked on 5 Nov 2026. Cloud services change often; check the official page before relying on a feature. All references