Home > AI Solutions > Gen AI > White Papers > Product Support Quick Notes Retrieval > Introduction
In the domain of tech-support and services, Knowledge Base (KB) articles are indispensable resources for both customers and support agents. These articles typically encompass step-by-step troubleshooting guides, supplemented with multimedia aids such as video guides and images, aimed at providing self-support instructions to common technical issues. For example, Dell Services is a repository of KB articles that address a broad spectrum of "How to" questions, providing general troubleshooting steps for common technical issues. There are also highly specialized articles like Product Support Quick Notes (PSQN), which tackle specific issues unique to hardware configurations or product lines. Specialized articles, such as the "Yellow Paint Defect," known only to Inspiron 7348 models, provide targeted diagnostic and troubleshooting steps for prominent issues associated with specific brands or models.
The diverse nature of these articles, coupled with the varying levels of complexity and specificity, poses significant challenges in Information Retrieval (IR). Some KB articles are designed exclusively for support agents and contain advanced diagnostic procedures that are not publicly accessible. The sheer volume of over 100,000 available articles, translated into over 20 languages, compounds this issue. Therefore, efficiently identifying the most relevant articles based on symptom descriptions or specific keywords becomes a critical task.
To address this challenge, Dell Technologies Services (DTS) introduce an improved PSQN search facility, which leverages contemporary embedding-based IR techniques. This solution is designed to pinpoint and surface the ten most pertinent PSQN articles that align with a given symptom description and brand or model specification. DTS's approach enhances the efficiency of issue resolution by enabling support agents to quickly access the most relevant information, thus reducing the time spent sifting through an extensive array of articles. Additionally, DTS has automated much of the data management process, minimizing human intervention while maintaining, and even surpassing, the accuracy of previous models. This document presents the detailed implementation of the solution, highlighting its architecture, technical details, and the substantial business value it delivers in streamlining support operations.