How do you approach GenAI safety in consumer products?

[INTERVIEWER] How do you approach GenAI safety in consumer products? Here is the core question. How do you approach GenAI safety when building a consumer product? And here's the one thing that separates a strong answer from an average one. The average candidate treats safety like a disclaimer, a line of small print at the bottom of the screen saying this can make mistakes.

The strong candidate treats it as a system you can point at, with distinct layers and clear owners. Safety isn't just a promise to be responsible. It's an engineering stack you build and continuously measure. So what's the interviewer actually testing? They want to know if you can hold two opposite things in your head at once. An unsafe product loses trust and gets pulled.

An over-safe product is slow and completely unusable. By the end of this you'll be able to lay out a defence-in-depth stack and put four real metrics on it, committing to a clear posture instead of hand-waving about being careful. Let's start with the frame, because most people frame this wrong. The question isn't whether the product should be safe in the first place.

Obviously it should be. The real question is how much friction and cost you spend on safety, and where you spend it. Too little, and you ship harm and people get hurt, losing the market's trust overnight. Too much, and every request gets three classifiers and a warning screen. The thing feels like it's wearing a straitjacket, so users leave for a competitor that feels freer.

So safety is really a resource allocation decision spread across the request path. Say that out loud in the room. It reframes the whole answer from moralising into engineering. Now the layers, because a serious answer names a stack, not one trick. Picture the path a single message takes. First, the input layer. Before the request even reaches the model, a prompt classifier catches the clearly disallowed stuff like self-harm, child sexual abuse material, weapons instructions, and targeted harassment.

Second, the model layer. That's RLHF and constitutional-style training, so safe behaviour and sensible refusals are baked into the weights themselves, plus a system prompt that sets the boundaries for the session. Third, the output layer. The model can still produce something bad even from a clean prompt, so you run a classifier on the generated text before it ever renders on screen.

Fourth, the product layer. Rate limits and age gating, plus UX that sets expectations up front by explaining this can be wrong and providing a button to report it. And fifth, the feedback layer. A reporting flow and a standing red-team programme, alongside an incident review that turns every real failure into a new test case. Five layers, each catching what the one before it missed.

This layered approach is the core of defence in depth. Then you make it measurable, because you cannot manage safety if you don't measure it. Here are four specific numbers I would track to make that happen. One, harmful output rate on a fixed eval set you run on every release. Two, false refusal rate, because over-blocking is a genuine product cost rather than a safe default.

Three, report volume per million messages, which is your early-warning signal. And four, time to mitigation, meaning how fast you can close a new jailbreak once it shows up in the wild. Without those four metrics, you are just relying on good intentions rather than running a real safety system. Now commit to a posture, because the interviewer wants a decision, not a shrug.

For a broad consumer product, my default answer would be that we block the catastrophic and warn on the sensitive, while allowing the rest with logging. Hard blocks are reserved for the small set of genuinely dangerous categories where a single miss is unacceptable. Everything else gets softer handling like a warning or a nudge, so you're not strangling normal use.

That posture is defensible, and it shows judgement rather than fear. And name the risks and the moat, because that's the senior move. First risk is over-blocking. A product that feels like a nanny pushes users to a rival that feels less preachy, so false refusals are a churn driver rather than a free win. Second risk is that adversarial users route around any single classifier, which is exactly why no one layer is ever enough.

Now the moat, and this is the part that turns safety from a cost into strategy. Every reported failure becomes an eval case. Your red-team corpus compounds. Over a year you've built a library of real-world attacks and edge cases that a new entrant simply doesn't have. That corpus is proprietary, and it makes your safety system measurably harder to beat than a copycat's.

Let me make this concrete with a companion chat app, which is exactly the kind of product where safety really bites. Someone types a message that signals self-harm intent. The input classifier catches it and routes to a crisis resource card instead of a model completion. And notice, that's a hard rule, not a model judgement, because the cost of a miss here is a human life, so you don't leave it to a probabilistic system.

But everyday emotional venting, like someone having a rough night, gets through, because blocking it would fail the exact user you're trying to help. You run a two thousand case eval set every week. Harmful output rate holds under nought point one percent, and false refusal rate stays under two percent. Then one week a new jailbreak appears where people wrap a bad request in a roleplay frame, asking the model to pretend it is a specific character.

Report volume jumps from four per million messages to forty. Your incident review pulls a hundred and fifty roleplay variants into the eval set and retrains the input classifier inside the week. Time to mitigation is about five days. And that fresh corpus of real jailbreaks is now an asset a competitor starting today simply cannot copy. A good interviewer won't just stop at the initial stack.

They will push you on how to decide where a hard block ends and a warning begins. That line is a product decision, not a safety one, and here's how I would draw it. Reserve hard blocks for the categories where a single miss causes irreversible harm, like self-harm instructions, weapons, and child safety. For everything in the grey zone, like political content, medical questions, or edgy humour, you warn and log rather than block.

That's because the cost of a wrong block there is a frustrated user and a churn event, while the cost of a wrong allow is usually recoverable. Then they'll ask the harder one about what happens if your own classifier is biased and over-blocks one group's normal speech. That's real, and it's exactly why false refusal rate has to be segmented by language, dialect, or topic, rather than reported as one global number.

A two percent false refusal rate that's actually eight percent for one community is a fairness failure hiding inside a healthy-looking average. So you break the metric down and watch the tail, treating a spike in one segment as an incident just like a jailbreak. And one more they like to throw at you is who owns this. Name it.

The input and output classifiers need an owner, just like the eval set, while incident review requires an on-call rotation, because safety with no named owner is safety that rots the first quiet week. Then there's the build versus buy angle. Early on you'll lean on off-the-shelf moderation APIs, and that's fine, but the moment your product has a distinctive risk surface, like a companion app's emotional edge cases or a coding tool's exfiltration risk, you have to build your own eval set on your own traffic.

A generic classifier cannot see the failures that are specific to what you shipped. Saying all that out loud tells them you've run this in production, not just drawn it on a whiteboard. So here's what makes them lean in. First, you named a layered stack with owners and metrics, instead of saying we take safety seriously, which tells them nothing.

You also treated false refusals as a real cost. That's product judgement, not just risk aversion, and it's rarer than you'd think. And finally, you connected safety to a data moat, which means you see it as strategy rather than compliance overhead. That last one is what separates a product manager from a policy hire in their eyes. Now let's look at the common traps.

The first one is answering that we filter bad content as if a single classifier is the whole story. It isn't, and it tells them you've never actually shipped this. The second trap is ignoring over-blocking, so your product ends up technically safe but completely unusable. And the third is having no metrics at all, which means you literally cannot tell whether the system works or whether it's improving.

Without measurement, you're just guessing, and the interviewers will easily spot that gap. Let's pull it together. Safety is a resource allocation decision across the request path, not a disclaimer. You build it as five layers: input, model, output, product, and feedback. You put four numbers on it: harmful output rate, false refusal rate, reports per million, and time to mitigation.

You commit to a posture where you block the catastrophic and warn on the sensitive, while allowing the rest with logging. And the failure log you build along the way is your moat. So the one line to carry into the room: safety is a measurable defence-in-depth stack whose failure log is your moat, not a disclaimer at the bottom of the page.