Decide and defend
Enough AI architecture to make the scope, cost, and feasibility call.
A Product Builder is not an ML engineer. You don't write retrieval pipelines or tune embeddings. Engineering owns that. What you own is the decision: which primitive fits the problem, what it costs at scale, and where it will fail.
Know the menu cold. Rules are cheap, deterministic, and brittle. A single model call is predictable. Retrieval grounds answers but is sensitive to freshness. An agent with tools handles multi-step work and is harder to test. Fine-tuning is for when prompting has capped out and you have stable labeled data.
Before that decision, map the workflow the feature lives inside. Where's the trigger, where's the human checkpoint, where does automation stop. The map is what makes the primitive choice defensible.
And when an agent acts, gate its autonomy on how reversible the action is and how strong the evidence is. Never on how confident the model sounds.
Today
Take the next AI feature on your list and write three lines: the primitive you'd pick, the cost and latency you expect at scale, and the one failure mode you'll monitor.
Done when: You could defend those three lines in a five-minute exec review without notes.
Get one lesson a day instead
Same lessons, one a day, so the tasks actually happen.