Retail QA Automation Framework for LLM-Generated UX: Testing Conversational Commerce Interfaces for Compliance, Clarity, and Consistency

Authors: Mohnish Neelapu

DOI: https://doi.org/10.37082/IJIRMPS.v13.i6.232852

Short DOI: https://doi.org/hbfbvn

Country: India

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Abstract: Conversational AI is reshaping retail at a breakneck speed. However, large language model (LLM), generated interactions that are compliant, clear, and consistent still pose a big challenge in the quality assurance of social interactions. This document introduces a Retail QA Automation Framework that through a modular, end, to, end pipeline systematically evaluates conversational commerce interfaces that are LLM, driven.

The presented framework features the integration of realistic query generation, standardized LLM execution, hybrid rule, based and transformer, based response analysis as well as automated scoring to detect the responses that align with compliance, clarity and consistency dimensions.

A rich dataset of real, public, and synthetic dialogues from the retail domain, with paraphrases, personas, and noise, injected variations, allows for the robust evaluation of product inquiries, promotions, returns, complaints, and policy clarifications.

Research findings over GPT, 4, LLaMA, 2, Falcon, 7B, and a retail fine, tuned GPT model suggest that domain, specific fine, tuning substantially elevates policy adherence, readability, and multi, turn coherence, while the lesser models provide advantages in terms of speed and cost.

Through the provision of reproducible evaluation protocols, comprehensive violation analysis, and iterative feedback loops, the proposed framework offers a scalable approach to benchmarking and enhancing LLM, powered retail interactions. It makes conversational commerce safer, more accurate, and more user, friendly, at the same time maintaining enterprise, grade quality assurance in modern retail ecosystems.

Keywords: Retail QA Automation, Large Language Models (LLMs), Conversational Commerce, UX Evaluation, Policy Compliance, Clarity Assessment, Consistency Analysis, Transformer Models, Multi-Turn Dialogue Testing, Semantic Evaluation, Rule-Based and ML Hybrid Framework, Retail Fine-Tuning, Automated Response Analysis, Conversational AI Quality Assurance, Synthetic Query Generation.

Paper Id: 232852

Published On: 2025-12-11

Published In: Volume 13, Issue 6, November-December 2025

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