Building an AI feature in a notebook is one thing. Running it for thousands of users, every day, without breaking your error budget or your wallet, is something else entirely.
Here's the checklist we walk through before any LLM-powered feature goes live.
1. Define the eval before you define the prompt
If you can't measure what "good" looks like, your prompt iteration is just vibes. Build an evaluation set early — even 50 hand-picked examples is enough to catch regressions when you swap models or tweak instructions.
2. Cache aggressively
Most production LLM traffic is repetitive. Cache by (prompt, model, params) key with a sensible TTL. We routinely see 40–70% cost reductions on read-heavy features.
3. Stream responses by default
User-perceived latency drops dramatically when you stream tokens. Anything over ~800ms without feedback feels broken — streaming buys you several seconds of headroom.
4. Have a fallback
When the model fails — and it will — what happens? A graceful fallback (cached answer, simpler model, deterministic path) is the difference between a blip and an incident.
5. Log everything, redact carefully
You need full traces to debug, but you also can't ship PII into your analytics warehouse. Build the redaction layer before you turn on logging.
The prototype shows it can work. The production system proves it will work — reliably, affordably, and safely.