AI in the Stack
You've been using AI to build all semester. Now: AI as an ingredient inside the products you design.
Hour 1 — Models as materials
- LLMs demystified: prediction machines, not databases — why they hallucinate
- The AI API call: it's just another API (prompt in, text out, tokens as billing)
- What models can do well (language, classification, generation) and badly (facts, math, consistency)
- Prompts as design artifacts: system prompts are product decisions
- The current landscape: models, wrappers, agents — telling substance from hype
- Designing AI features responsibly: failure states, trust, disclosure
Hour 2 — Studio
- Exercise: Exercise - Add an AI FeatureExercise - Add an AI Feature
Put a model inside your product
Add one genuinely useful AI feature to something you've already built this semester. The bar: it must be better because it's AI, not AI for its own sake.
Ideas
... — add one genuinely useful AI feature to something you've built this semester - Crit: is this feature better because it's AI, or is it AI for AI's sake?
Homework
- Start thinking about final project ideas — teams and topics get decided in Lecture 10Lecture 10
How Software Gets Built
The people part: how ideas become products, and how teams avoid chaos.
Hour 1 — The lifecycle
The SDLC: idea → requirements → design → build → test → ship → maintain
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