Knowing when to trust an AI model
A mid-sized UK market research company built digital twins using large language models to simulate how real consumers would answer surveys. The team believed in the potential but before committing further investment, the board needed evidence they could trust.
I designed an evaluation framework to test how closely the model’s simulated responses matched real survey data, then ran it against a representative set of cases to produce a score the board could act on.
The model matched real responses with 85% accuracy, comparing the distribution of simulated answers against real survey results. That was enough evidence for the board to approve continued investment, but not enough to trust everywhere equally. I recommended they deploy it first in the specific contexts where it was most reliable, and expand coverage from there as the evidence allowed.
The next step will be to calibrate the model’s own internal confidence against how often it’s actually correct. This will let the model evaluate it’s own accuracy and flag blind spots on a case-by-case basis. Then the team will know exactly when it’s safe to trust the digital twins and when human responses are required.
Convince them with evidence
Find out why LLMs aren’t databases, and what that means for trusting what they tell you.