Module 2 · Proof: build useful evidenceWed, Sept 2360 minshowcase

Proof review

Show the workflow, the evidence log, and the remaining questions. Use the review to set the stewardship work.

Faculty: Prof. Xiao-Li Meng · All faculty

Learning objectives

Live demoPrototype feedback loop

🧠 Live demo🧠 Iterate on feedback

1. Read
Read the session guide above. Note the ideas that change how you see your organization's current AI question.
2. Test
Take the module quiz in Learn & Memorize to check understanding.
3. Drill
Run the spaced-repetition flashcards to lock in the terms from this module.

Supervised vs unsupervised learning
Supervised = model learns from labeled examples (X→Y). Unsupervised = model finds patterns in unlabeled data (clustering, segmentation).
LLM
Large Language Model: a deep network trained on vast text to predict next tokens; the engine behind generative AI. Limits: hallucination, context window, no true reasoning.
Agentic AI
Systems that use a model plus tools, memory, and loops to pursue a goal with limited supervision: vs a single model call. Example: Anthropic's agent patterns.
Data silos
Data trapped in separate systems/teams. The #1 blocker to AI: if data isn't accessible and quality, no model can save the use case.
RAG
Retrieval-Augmented Generation: feeding an LLM relevant retrieved context at query time to ground answers in your own data and reduce hallucination.
Build vs buy vs partner
Build = custom IP/control; Buy = vendor SaaS (faster, but vendor lock-in & data risk); Partner = co-build with consultants. Decision depends on data reality and core competency.

Build for Module 2: Use Case Map + Technical Brief + Prototype Demo