Q&A Support Chatbot for Broadcast Advertising Documents

Korea Broadcast Advertising Corporation (KOBACO) developed a RAG-based Q&A service targeting relevant documents and integrated it with a chatbot system to enhance operational efficiency.

pays

Corée du Sud

paliers de gouvernement

National

chronologie

1–2 ans

coût du projet

TBD

organisation

Korea Broadcast Advertising Corporation (KOBACO)

ampleur de l'impact

Programme pilote/petite équipe

catégories

Prestation de services et expérience citoyenneTraitement et administration de documents

type de technologie

ChatbotsLLM (ex. Gemini, Claude, Microsoft Co-Pilot)

Overview

The project was initiated to improve the civil complaint handling process, as staff members faced difficulties in providing rapid responses due to repetitive and time-consuming manual searches. Korea Broadcast Advertising Corporation (KOBACO) developed a RAG-based Q&A service targeting relevant documents and integrated it with a chatbot system to enhance operational efficiency.

Project description

To support the broadcast advertising chatbot, relevant documents were restructured into search-optimized formats. Generative AI was used to automatically generate chatbot queries, leading to the development of a specialized chatbot service that combines RAG technology with KOBACO’s advertising data.

The service, which supports staff in responding efficiently to complaints, is classified into two types:

Chatbot-based responses: Provides answers based on pre-trained knowledge from existing documents (internal data, laws, precedents, etc.).
RAG-based SearchGPT: For queries outside the training set, it provides highly relevant content using RAG-powered search capabilities.

Key technological features

Data Collection: Collected internal data from KOBACO (Korea Broadcasting Advertising Corp.), along with external laws and legal precedents via APIs and public web pages
OCR Implementation: Applied Optical Character Recognition (OCR) models to process scanned documents and image data, enabling text extraction from existing archives.
RAG Utilization: Employed Document Layout Analysis (DLA) models to extract content and structure internal documents for efficient RAG (Retrieval-Augmented Generation) learning.

Expected outcomes

By replacing repetitive and exhaustive tasks with AI, the service is expected to create a work environment where employees can focus on more professional and high-value tasks.

Launch year: 2024

Attribution

Case study courtesy of the National Information Society Agency, Republic of Korea, Apolitical's Content Partner. Sourced from Use Cases of Public AI Service, Vol.2, https://eng.nia.or.kr/site/nia_eng/ex/bbs/View.do?cbIdx=31975&bcIdx=29182&parentSeq=29190