⭐ Protocol Overview: Evaluating AI‑Generated Consumer Guidance Across Six Applied Domains
AskAFriend Publishing Research Team Last Updated: June 2026
This page provides a high‑level overview of the registered research protocol evaluating how large language models (LLMs) generate consumer guidance across six applied domains. The full protocol paper is titled: A Domain-Agnostic Framework for the Systematic Evaluation of AI-Generated Consumer Guidance.
Abstract
Large language models are increasingly used by consumers seeking guidance on complex, high‑stakes decisions in areas such as legal rights, insurance claims, home purchasing, consumer protection, home repair, and health advocacy. Despite widespread adoption, no unified evaluation framework exists to assess the accuracy, completeness, safety, and actionability of AI‑generated guidance in these domains.
This protocol describes a structured meta‑analysis designed to evaluate AI performance across six applied consumer domains using a standardized prompt‑response‑evaluation pipeline. The study employs a three‑phase design:
- development of a calibrated prompt library,
- systematic collection of AI responses from multiple LLMs, and
- dual‑track evaluation combining expert scoring with an automated scoring pipeline.
Outcomes include domain‑specific scorecards, a validated cross‑domain rubric, and an error taxonomy for consumer AI guidance. You can download a business-scoped overview here:
Study Purpose
The purpose of this study is to create a rigorous, reproducible framework for evaluating AI‑generated consumer guidance. Existing AI benchmarks focus on academic or technical tasks and do not reflect the real‑world questions consumers ask when navigating legal, financial, health, and home‑related decisions. This research fills that gap by applying a unified methodology across six domains where AI errors can carry financial, legal, or health consequences.
Domains Evaluated
This study evaluates AI‑generated guidance across six applied consumer domains:
- Legal Literacy
- Insurance Navigation
- Home Buying
- Consumer Fraud
- Home Remodeling
- Health Advocacy
Each domain includes 45 consumer‑style questions written at three complexity tiers.
Research Questions
The study is organized around three primary research questions:
RQ1: How accurate, complete, and actionable is AI‑generated guidance across six consumer domains when evaluated using a standardized multi‑dimensional rubric?
RQ2: Do AI performance patterns vary systematically by domain, and which domains show the greatest strengths and vulnerabilities?
RQ3: Can a standardized prompt‑response‑evaluation pipeline produce reproducible, publication‑quality findings across heterogeneous consumer domains?
High‑Level Method Summary
This study follows a three‑phase design:
Phase 1 — Prompt Library Development
A calibrated library of 270 prompts is developed across six domains and three complexity tiers (factual, procedural, and judgment‑dependent). Prompts reflect real consumer information‑seeking behavior.
Phase 2 — AI Response Collection
Prompts are submitted to multiple leading LLMs under standardized conditions. Responses are collected verbatim with metadata to support reproducibility.
Phase 3 — Evaluation Framework
AI responses are evaluated using a dual‑track system:
- Expert panel scoring using a six‑dimension rubric (Accuracy, Completeness, Actionability, Safety, Jurisdiction Sensitivity, Transparency)
- Automated scoring pipeline validated against expert judgments
This high‑level summary reflects the study design without disclosing full methods, tables, or analysis plans.
Status
This protocol is currently available now. The reference model has also been published.
Follow the Study
AskAFriend.com will publish:
- methodological updates
- version history
- scoring framework summaries
- non‑sensitive research artifacts
- final results after peer review
This page will be updated as the study progresses.