RAG Foundations
6 Labs
Build a retrieval-augmented generation system step by step through measured engineering decisions.
Build → Retrieve → Rank → Query → Measure → Experiment
Open Learning Path →Experience
Interactive AI Labs
Learn AI Engineering through guided interactive experiences.
20 LabsGuidedMeasured executionsNo API keys required
Choose a learning path, then open a lab to explore Architecture, Guided Demo, and Code through a measured Cookbook run.
Available
Learning paths you can start today.
AI Agents Foundations
5 Labs
Learn the agent/tool loop, evaluate recorded runs, manage explicit plans, then see how memory persists information across interactions.
Foundations → Evaluation → Planning → Memory
Open Learning Path →Model Context Protocol (MCP)
4 Labs
See how an MCP client initializes with a server, discovers tools, resources, and prompts, then composes those results and requests Sampling through the client.
Discovery → Resources → Prompts → Composition
Open Learning Path →Knowledge Graphs
5 Labs
Explore how RDF triples become traversable knowledge, how SPARQL queries and updates the graph, how GraphRAG grounds answers in retrieved subgraph evidence, and how construction validates proposed facts before RDF commit.
RDF & Graph Traversal → SPARQL & Graph Queries → SPARQL Updates & Graph Mutation → GraphRAG → Graph Construction
Open Learning Path →Planned
More engineering learning paths are currently planned and will be added over time.
LLM Engineering
Interactive labs covering prompt engineering, evaluation, caching, production patterns and optimization.
Planned · October 2026
Want to run the code yourself?
Every interactive lab is backed by a runnable DataAIHub Cookbook example. The Lab explains the execution visually; the Cookbook lets you run and modify it locally.
Explore the Cookbook →