Summary of From Rag to Qa-rag: Integrating Generative Ai For Pharmaceutical Regulatory Compliance Process, by Jaewoong Kim (sungkyunkwan University) et al.
From RAG to QA-RAG: Integrating Generative AI for Pharmaceutical Regulatory Compliance Process
by Jaewoong Kim, Moohong Min
First submitted to arxiv on: 26 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study introduces a chatbot model that utilizes generative AI and Retrieval Augmented Generation (RAG) to navigate complex guidelines in the pharmaceutical industry, thereby reducing the need for human resources. The proposed Question and Answer Retrieval Augmented Generation (QA-RAG) model is designed to search for relevant guideline documents and provide accurate answers based on user inquiries. In comparative experiments, QA-RAG demonstrated significant improvements in accuracy compared to conventional RAG methods, showcasing its potential for regulatory compliance beyond the pharmaceutical industry. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a chatbot that helps with complex guidelines in the pharmaceutical industry. The chatbot uses artificial intelligence and a special method called Retrieval Augmented Generation (RAG). It finds relevant documents and gives answers based on what people ask. The researchers made their work public so others can use it too. |
Keywords
» Artificial intelligence » Rag » Retrieval augmented generation
