AI Literacy for Operators

A $30 foundation course for operators who need to understand what AI systems can do, where they fail, and how to judge claims before buying tools. Covers model basics, prompt vocabulary, workflow fit, source grounding, privacy, and evaluation habits. Built for non-engineers, founders, and team leads who need practical literacy before implementation. Core sources: - https://arxiv.org/abs/1706.03762 - https://aiindex.stanford.edu/report/ - https://www.nist.gov/itl/ai-risk-management-framework

Curriculum

1.
What modern AI systems are good at
A practical overview of language models, multimodal systems, assistants, agents, and workflow automation without math-heavy detours.
2.
Where AI systems fail
Hallucination, brittle instructions, context limits, stale knowledge, privacy exposure, and why human review still matters.
3.
Operator vocabulary
Tokens, context, tools, retrieval, evals, guardrails, model selection, and the language needed to ask vendors better questions.
4.
AI fit versus automation theater
How to distinguish high-leverage workflows from demos that will not survive real operations.
5.
Source-backed AI reading habits
How to compare vendor claims against primary docs, benchmarks, and independent reports.
6.
First operating checklist
A lightweight checklist for privacy, security, success criteria, and rollout readiness before adopting a tool.