Learning path · Foundations · 01
What Is Generative AI
AI systems that create new text, code, images, or audio by learning statistical patterns from large datasets instead of executing only hand-written rules.
Why it matters
- Separates generative systems from classical predictive ML and rules engines.
- Sets expectations: fluency is easy; reliability and grounding take architecture.
- Frames every later topic as a way to make generation useful, auditable, and safe.
Key ideas
- Generation vs prediction
- Foundation models
- Capability and risk travel together
Generative AI produces artifacts that resemble its training distribution—drafts, summaries, code, designs. That power is not the same as knowing your business facts. Treat the model as a reasoning engine with parametric memory, then surround it with retrieval, tools, evaluation, and guardrails. The rest of this path is applied architecture: how to ground outputs, control cost, orchestrate agents, and ship systems that survive production traffic rather than demo well once. Start every project by listing what must be grounded externally versus inferred, then design observability before scaling traffic.
Updated 2026-08-09 · Full learning path