Almost every product is adding an AI feature right now: a summarizer, a “draft this for me” button, a natural-language search, a support copilot. The build has never been easier, an LLM API call is a few lines. The hard part is that an AI feature fails differently from a normal feature. It doesn’t crash; it confidently returns something wrong, slow, or expensive. That changes how you ship it.

Here’s how to add an AI feature that’s genuinely useful without exposing you to the failure modes that make AI features embarrassing.

Scope it to something an LLM is actually good at

The first mistake is aiming too broad. “An AI assistant for our whole product” is a research project. “A button that drafts a reply from this thread, which the user edits before sending” is a feature you can ship this month.

Good first AI features share a shape: the output is assistive (a human reviews it before it counts), the task is bounded (summarize this, classify that, draft the other), and a mediocre result is annoying, not dangerous. Start there. Autonomy and high-stakes actions come after you’ve earned trust.

A scoping prompt:

I want to add [an AI feature that drafts customer-support replies]. Help me scope the smallest version that’s genuinely useful. What’s the input, what’s the output, where does a human stay in the loop, and what’s the worst plausible bad output? Push back if my scope is too broad for a first version.

Control cost and latency from day one

Two numbers will surprise you in production: the bill and the wait.

Every call costs money, and the cost scales with usage in a way normal features don’t, so cap it early. Set a per-user and per-account budget, cache identical requests, and pick the smallest model that does the job rather than the biggest one that does it slightly better. The wait is the other half. LLM calls are slow by web standards, often whole seconds, so stream the response, show a clear “thinking” state, and set a timeout with a graceful fallback. A spinner that hangs for eight seconds reads as broken.

Plan for the ways LLMs fail

Normal code fails loudly. LLMs fail quietly and plausibly. Build for it:

  • Wrong but confident. Keep a human in the loop for anything consequential, and make the AI’s output editable and clearly labeled as a draft, not a decision.
  • Prompt injection. If your feature reads user-controlled or third-party content (emails, documents, web pages), treat that content as untrusted. Don’t let it silently redirect the model into actions the user didn’t ask for.
  • Unavailable or rate-limited. The provider will have a bad day. Decide in advance what the feature does when the API returns a 429 or 500, degrade gracefully, don’t white-screen.
  • Cost spikes. A loop or an abusive user can run up a bill fast. Your budget caps are the backstop.

Roll it out behind a switch, always

You cannot fully predict an LLM’s behavior before real users hit it. Your test prompts are not your users’ prompts, and the feature will do something in production that it never did in staging. That’s the whole argument for shipping it behind a switch.

So you ship it dark and ramp it deliberately:

  1. Merge it off. The AI feature goes to production behind a flag, disabled. No user sees it yet.
  2. Turn it on for your team. Use it on real data for a few days. You’ll immediately find the prompt that produces garbage and the input length that blows your token budget.
  3. Enable for a small percentage of users. Watch three things: output quality (are people editing heavily or discarding entirely?), cost per user, and latency. Real usage reveals all three.
  4. Ramp as the numbers hold. 1% → 10% → 50% → everyone, pausing at any step where quality or cost surprises you.

The rollout percentage is the boring half of what that flag does. The other half is the kill switch. When a new model version regresses, or a prompt-injection report lands, or the provider changes its pricing overnight, you disable the feature in seconds without a deploy. For a feature whose behavior you don’t fully control, that instant off-switch is what makes shipping it defensible in the first place.

You can wire the gate in with one prompt:

Wrap this AI feature behind a feature flag that’s off by default and can be enabled per-account or for a percentage of users. If the flag lookup fails, default to off. Also add a separate flag that disables the feature globally as an emergency kill switch, independent of the rollout percentage.

The shape of a safe AI launch

Scope it narrow, keep a human in the loop, cap the cost, and stream the response so the wait doesn’t feel like a hang. Then assume it will misbehave anyway, and make sure that when it does you can turn it off faster than a user can screenshot it. That’s the difference between a controlled, reversible experiment and a leap of faith.

The teams that ship AI features well aren’t the ones with the best prompts. They’re the ones who can turn a feature off in one click while everyone else is drafting an apology.

ShipSilently gives your AI features an instant, edge-evaluated kill switch and percentage rollouts, so a surprising model never becomes a surprising incident. Start free.