Quiz — Session 9: Building AI Products

b. Product thinking starts from the problem, never from the technology. Questions A, C and D are legitimate, but they come later — otherwise we construct a solution in search of a problem.

c. A good AI product eliminates uncertainty visible and actionable : the user concentrates his rereading where it is useful. A hides information, B deceives the user, D destroys the value of the product (brutal degradation instead of graceful).

b. Approval = validation Before execution. Answer A describes the “override” level (posterior control). The higher the cost of an error, the more we move towards pre-approval, or even manual.

c. Huge volume ✅, a category error is easily corrected ✅, reclassification by agents provides a signal of improvement ✅. A fails on tolerance (zero admissible errors in payroll), B on volume (once/year), D on both.

b. Climbing is the transfer towards humans. This is a central metric: a rising escalation rate signals a deterioration in the perceived or actual quality of the product — often before other metrics move.

b. A discrepancy may be due to chance if the sample is small; and even validated, a progressive deployment (feature flag) limits the risk. A skips the steps, C is immobility, D has no methodological basis.

b. Monthly savings = 40,000 − 15,000 = €25,000. Breakeven = 50,000 / 25,000 = 2 months . Reflex to remember: always reason about the economy net (including the residual human cost of supervision and escalation), not on the API cost alone.

b. Trust is built by explaining what AI did (what sources, what processing), not how it works internally (A). Assurance without proof © and concealment (D) destroy trust the first error discovered.

c. “Buy” (off-the-shelf API) is the reasonable default for ~80% of cases: quick implementation, minimal maintenance, predictable cost. Fine-tuning is justified later , on proof: insufficient precision despite good prompting, volume which makes the economy favorable, training data available. A and D are disproportionate.

b. Recruitment is a high-risk use case (explicitly targeted by the European AI Act ⚠): high cost of error for individuals, potential bias (first name, address, etc.), obligation of transparency and recourse. The human in the loop cursor must be at least on “human approval” for rejections. A, C and D are side details.