The generative-AI playbook for product teams

Generative AI has crossed the line from demo to dependency. The teams getting durable value from it aren't the ones with the flashiest prototype — they're the ones who treated it as a product problem from the very first sketch.

The gap between a convincing demo and a feature people trust in production is enormous, and it's mostly made of unglamorous work: evaluation, guardrails, cost control, and a clear-eyed view of what the model is actually good at.

01Start with the job, not the model

Pick the narrowest task where a wrong answer is cheap and a right answer is valuable — summarising an internal document, drafting a first reply, classifying a ticket. These are forgiving places to learn. Resist the urge to build the all-knowing assistant on day one.

If you can't describe what “good” looks like for a feature, you can't ship it responsibly — and you certainly can't improve it.

02Build the evaluation harness first

Before you tune a single prompt, write the test set. A few dozen real examples with expected behaviour will teach you more than any vibe-check, and re-running them on every change catches regressions the moment they appear.

A useful eval set includes:

• Real inputs from your domain, not toy examples
• Clear pass/fail or graded criteria a human agrees with
• The edge cases and adversarial prompts you actually expect
• A cost and latency budget for every call

03Guardrails are a feature, not a tax

Retrieval that grounds answers in your own data, validation on structured outputs, and a graceful fallback when confidence is low — these are what turn a clever model into something a customer can rely on. Design them in; don't bolt them on after the first incident.

04Watch the unit economics

Token cost compounds quietly. Cache aggressively, route easy requests to smaller models, and measure cost per successful outcome rather than cost per call. The cheapest model that still passes your eval wins.

Ship the smallest useful thing, measure it honestly, and let the evidence — not the hype — decide what to build next.

Generative features reward the same discipline as the rest of software: scope tightly, measure honestly, and own the outcome. The playbook was never really about the model. It's about the product you build around it.