Research question
Exact estimand, scale, non-claims, and use boundaries.
Leading large language models encode broad patterns of language, facts, concepts, and reasoning. A structured study asks the same question across a documented model panel, then separates shared findings from model-family disagreement, run-to-run variation, and prompt sensitivity.
This is a measurement, not an oracle. Each study has a defined question, model population, evidence conditions, protocol, aggregation rule, and release. Its results describe the selected models under those conditions—not artificial intelligence as a single mind or an objective source of truth.
We predefine which models are asked, what information they may use, how the question is phrased, how repeated responses are aggregated, and what counts as a valid result. The measured population consists of defined AI models rather than people.
Exact estimand, scale, non-claims, and use boundaries.
A dated target population, documented panel, and inclusion rules.
The same question, evidence conditions, prompt variants, and order controls.
Repeated runs, accounting for failures and refusals, and family-level aggregation.
Model, prompt, order, and weight sensitivity with a reproducible release.
Median, disagreement, limitations, method sheet, and a publishable results page.
An application of the method to 65 public professional profiles using four model families, five configurations, and six adjustable criteria.
Send us the research question, intended audience, and desired use. We will assess whether it is suitable for an AI Poll, what panel and protocol are needed, and which publication format fits the project.
For public releases, the commissioning party and funding are disclosed; we do not guarantee a predetermined result.