Your AI learning map

You finished the literacy lane’s job: everyday smarts before fancy prompting tricks. You can spot AI in normal apps, define it without robot myths, pair human vs machine strengths, sort can/can’t claims, explain learning-from-examples, tell generate vs detect apart, hunt confident mistakes, notice skewed data, and scrub secrets before you paste. That kit is what keeps later skills from becoming cargo-cult “prompt magic.”

Think of your map as layers, not a race:

1. Literacy (Lane A) — where you are now. Noticing, judging, and staying safe around AI in daily life.
2. Conversation / prompting (next up). Clear asks, better drafts, follow-ups, and checking outputs — starting with your first real back-and-forth lessons.
3. Deeper builds later. Projects, workflows, and sharper specialty skills once the foundation doesn’t wobble.

You don’t “graduate” by memorizing jargon. You graduate when you catch yourself mid-week saying: “That’s a generative guess — verify,” or “Don’t paste the API key,” without a teacher in the room.

• Feeling behind because peers only talk tools and model names — your spine knowledge transfers across tool churn.
• Choosing the next lesson: prefer conversation practice next, not a random advanced bunny trail.
• Explaining your progress to a friend: “I finished literacy; next I’m learning to talk to AI on purpose.”

Skipping literacy to chase viral prompt screenshots is how people ship confident nonsense and leak data. Also, finishing Lane A doesn’t mean you never revisit it — bias and privacy keep showing up as tools change.

Say out loud (or sticky-note) three wins from this lane — one spotting skill, one judgment skill, one safety skill. Then open the next lesson on your path (first-conversation) and treat it like a lab, not a TED talk.