Shipping LLM features people actually trust

The demo is the easy part

Any team can get a language model to produce an impressive answer in a meeting. The hard part is getting a useful answer on the ten-thousandth try, for a customer you have never met, with data that changed this morning.

Start with evaluation, not prompts

Before we write a single prompt, we collect fifty to a hundred real examples with the answers a good human expert would give. That set becomes our test suite. Every prompt change, model upgrade or retrieval tweak runs against it before it ships.

Ground every answer

Most trust problems come from the model making things up. We connect it to the customer's own documents and data through retrieval, and we show sources next to every answer so people can check the work.

Design for the wrong answer

The model will be wrong sometimes. Good AI features plan for it:

→ Clear confidence signals in the interface

→ An easy way to correct or flag an answer

→ A human in the loop for anything with legal or financial weight

Measure what users do

Thumbs up and down are a start. We also track whether people copy the answer, edit it or abandon the task. Those signals tell us far more about trust than any benchmark score.