Cost, latency, success metrics & responsible AI

Shipping AI is a constant trade-off between quality, cost, and latency — and the PM owns those trade-offs plus how you measure success and manage risk.

Cost & latency

• Bigger models are smarter but slower and pricier per token; smaller/distilled models are cheap and fast. Route easy requests to small models, hard ones to large.
• Levers: shorter prompts/context, caching, streaming (perceived latency), and picking the smallest model that passes evals.
• Watch unit economics: cost per query × volume can dwarf infra — a feature must be worth its token bill.

Measuring AI product success

• Separate model quality (eval scores) from product success (did users get value?).
• Track adoption, task completion, acceptance/edit rate (how often users keep the AI's output), retention, and time saved — plus guardrails like complaint/escalation rate.
• A high eval score with low acceptance means the feature is good at the wrong job — fix the product, not just the model.

Responsible AI

• Bias & fairness: test across user segments; watch harmful or skewed outputs.
• Privacy & data use: be clear about what's sent to models and used for training; respect consent and regulation.
• Safety & transparency: guardrails for harmful content, disclose AI use, and provide human recourse for high-stakes decisions.
• Regulatory readiness: know your product's risk tier under rules like the EU AI Act — disclosure, logging, and human-oversight duties are now launch gates.