Confronting Hallucination: A Critical Challenge for Responsible AI

Generative AI has brought forth a multitude of powerful capabilities and diverse applications. However, a significant challenge arises, particularly in sensitive, high-value, and high-risk use cases, in the form of hallucination – the generation of factually incorrect information by Language Model Models (LLMs).

Our whitepaper introduces a solution in the form of the Hallucination Vulnerability Index (HVI), an evaluation mechanism designed to provide a comparative assessment of vulnerabilities in LLMs. This proposed framework, centered around Advanced Prompt Engineering and advanced Retrieval Augmented Generation (RAG), empowers enterprises to effectively mitigate the risks associated with hallucination in AI systems, thereby enhancing trust and efficacy in GenAI deployments.

Key Insights:

• A comprehensive exploration of Hallucination Orientations, Categories & Degrees
• Understanding the Hallucination Vulnerability Index (HVI)
• Introduction of the Factual Entailment (FE) Model for automatic detection of Hallucination
• Leveraging Advanced Prompt Engineering to prevent Hallucination
• Implementing Advanced Retrieval Augmented Generation (RAG) for Hallucination mitigation

Download our whitepaper to gain insights into how the adoption of our proposed framework and RAG architecture can effectively address the risks associated with hallucination in your AI systems, ensuring accuracy and reliability in your GenAI deployments.