Evaluating AI-Generated Consumer Guidance Across Six Applied Domains

This paper is the Reference Implementation of the Universal Core Framework v1.0 (Walcher 2026a), a registered protocol that instantiates the framework's evaluation methodology across six empirical consumer domains: legal literacy, insurance navigation, home buying, consumer protection, home remodeling, and health advocacy. Classified as a Full Implementation Study, the design comprises three phases: development of a calibrated prompt library of exactly 270 prompts (15 per complexity tier × 3 tiers × 6 domains); standardized collection of responses from five leading large language models under disclosed conditions; and dual-track evaluation combining domain-expert review with an automated LLM-as-judge pipeline, calibrated in two pilot domains. Responses are scored on the framework's six evaluation dimensions — Accuracy, Completeness, Actionability, Safety, Jurisdiction Sensitivity, and Transparency — using an anchored five-point scale. Behavioral signatures are coded systematically per framework rules. The study supports inferential conclusions and effect-size interpretation within the framework's claim-strength governance. Planned outputs include domain-specific performance scorecards, human–automated concordance analysis, a consumer-AI error taxonomy, behavioral-signature patterns, a reusable prompt library, and findings on five pre-registered hypotheses. The companion framework paper specifying the full methodology is Walcher (2026a). AI evaluation, consumer guidance, large language models, registered protocol, reference implementation, behavioral signatures, applied AI, reproducibility, prompt-response evaluation