Start from the assumption you won’t rewrite
The biggest mistake teams make is treating “add AI” as a re-architecture. It almost never is. A live app already has users, data, auth, and workflows that work — the goal is to bolt an intelligent capability onto that surface with the smallest possible blast radius. Treat the language model as one more backend dependency, wrap it in your own API, gate it with a flag, and give it a fallback. Done this way, adding AI is an additive change you can turn off in seconds, not a rebuild that risks the whole product.
The prompt-API proxy pattern
Never call the provider from the browser. Instead add a thin server endpoint —
/api/ai/<feature> — that owns the prompt template, injects the relevant
context, calls the model, and returns a clean result. This proxy is where all your
control lives: the API key stays secret, you enforce per-user rate limits and
spend caps, cache identical requests, validate and sanitise both input and
output, and log prompt-plus-response pairs so you can evaluate quality later.
Because the prompt and model choice live server-side, you can swap GPT for Claude,
tune the system prompt, or add retrieval without shipping a single client update.
The UI just calls your endpoint and renders the result.
Roll out behind a flag, degrade gracefully
Wire every new AI feature to a feature flag and ramp it: 5% of users, then 20%, 50%, 100%, watching error rate, latency, and cost per request at each stage. If something spikes, flip the flag off — instant, no deploy. Equally important is graceful degradation: models time out, rate-limit, and occasionally return garbage, so the AI path must always have a fallback to the behaviour the app had before. Set a timeout (a few seconds for interactive features), catch failures, and quietly route the user to the manual form, a cached answer, or a retry prompt. The principle is that AI is an enhancement that can vanish without breaking the core workflow — never a hard dependency on someone else’s uptime.
Guard costs and A/B test the value
Two failure modes sink retrofit projects: runaway cost and features that don’t actually help. Guard cost with per-user and per-day token budgets enforced in the proxy, aggressive caching of repeat requests, and the smallest model that meets the quality bar — most features do not need the flagship. Then prove value with a real experiment. Split traffic between the AI experience and the existing manual one, and measure an outcome that matters: task completion, time saved, conversion, or support-ticket deflection. AI features dazzle in demos and frequently fail to move the metric; the A/B test is what tells you to ship it, tune it, or kill it. Ship the smallest useful version, measure honestly, and expand only what earns it.