Shipping LLM features that survive contact with real users

Every LLM feature demos beautifully. You wire up a prompt, it answers three test questions perfectly, and everyone in the room nods. Then it meets a thousand real users typing things you never imagined - and the gap between "impressive demo" and "dependable feature" turns out to be most of the work.

Here's the checklist we run before any AI feature we build touches production.

1. Evaluate on your real data, not the happy path

"It seems to work" is not a launch criterion. We build an evaluation set from real inputs - including the messy, adversarial and empty ones - and measure accuracy before anyone relies on the output. The goal is to be able to say "it's right 96% of the time, and here's exactly what happens the other 4%," not to cross our fingers.

2. Design the failure mode first

Models are non-deterministic and occasionally confidently wrong. The question is never "will it fail?" but "what happens when it does?" Every feature we ship has an answer:

• Graceful degradation - a fallback to deterministic logic or a human when confidence is low.
• Cited sources - so users can verify rather than trust blindly.
• Guardrails - validation on the output before it ever reaches a user or a database.

3. Put a number on the cost before you launch

A feature that costs $0.02 per call is free at demo scale and a five-figure monthly surprise at product scale. We model token cost per user action up front, then design against a budget - caching, batching, and picking the smallest model that passes evaluation rather than the biggest one that impresses.

This is exactly the work behind our own AI platform, and it's what we bring to every AI integration we ship for clients - in production, with evaluation and monitoring, not a demo that degrades the moment real users arrive.

If you are planning an LLM feature of your own, our AI integration service covers exactly this: build, evaluate, monitor, and keep the costs sane.