RAG System

Farhan Habib Faraz, a developer, created a knowledge base that inadvertently provided false information to approximately 10,000 customers. The system, utilizing a Retrieval-Augmented Generator (RAG) model, generated convincing but inaccurate responses. The lies were not random, indicating a flaw in the system's design. Faraz discovered the issue and attempted to fix it, but initial solutions were unsuccessful. After realizing the scale of the problem, he implemented a new prompt engineering technique that effectively resolved the issue. The fix involved modifying the system's prompt to prioritize truthfulness. The correction significantly improved the accuracy of the knowledge base, as evident in before-and-after comparisons of real conversations. This incident highlights the challenges of ensuring truthfulness in artificial intelligence systems, particularly those using RAG models. The experience provides valuable insights into the development of more reliable and trustworthy AI-powered knowledge bases, a crucial aspect of the growing industry.