ProductQA: An E-commerce Review QA Error Correction System Based on Dual-Agent Verification Loop

Project Basic Information

• Original Author/Maintainer: PaulImmanuel
• Source Platform: GitHub
• Original Title: Automated-Error-Correction-in-Product-Review-QA-Systems-Utilizing-a-Dual-Agent-Verification-Loop
• Original Link: https://github.com/PaulImmanuel/Automated-Error-Correction-in-Product-Review-QA-Systems-Utilizing-a-Dual-Agent-Verification-Loop
• Release Date: 2026-05-31

Core Insights

ProductQA is an end-to-end generative AI framework designed for e-commerce product review QA scenarios. Through the collaborative loop of a verifier agent and a corrector agent, it ensures the natural fluency of answers while strictly maintaining factual consistency with the original reviews, providing a practical solution to the "hallucination" problem of large language models.

In e-commerce scenarios, traditional retrieval-based QA systems can ensure factual accuracy but lack coherence in answers; pure generative solutions are fluent in expression but prone to producing "hallucinated" content that contradicts facts. ProductQA needs to balance the advantages of both: it must understand the user's question intent while ensuring answers are strictly based on actual product review data, avoiding fabrication out of thin air.

The core innovation of ProductQA lies in its dual-agent collaboration mechanism:

1. The baseline generation module produces an initial answer;
2. The verifier agent compares it with the original reviews to identify factual deviations;
3. The corrector agent re-synthesizes an accurate and natural answer based on the original reviews;
4. The verification-correction loop iterates until the output meets the factual accuracy standards.

As a quality gatekeeper, the verifier agent uses logical reasoning to check each factual claim in the generated content. Unlike simple keyword matching, it deeply understands semantics and judges whether a statement is consistent with the review information (e.g., checking if the answer "battery life is about 10 hours" is supported or contradicted by reviews), and can capture subtle errors that traditional methods find hard to detect.

When the verifier detects deviations, the corrector agent does not delete suspicious content; instead, it extracts key information points based on the original review context, reorganizes the language to generate an answer that is both accurate and fluent, preserving the language advantages of generative AI and providing users with easy-to-understand content.

The design of ProductQA has reference significance for fields such as e-commerce, customer service, and knowledge base QA:

• In e-commerce scenarios, accurate information directly affects purchasing decisions and can avoid returns, negative reviews, and legal risks;
• Its "generate-verify-correct" pipeline model provides an implementable quality control framework for enterprises to deploy generative AI, helping balance innovation and reliability.