When it comes to RAG-based AI tools, one of the crucial aspects to consider is the accuracy of the content within the custom database. According to Joel Hron, a global head of AI at Thomson Reuters, it is not just about the quality of the content itself, but also the quality of the search and retrieval of the right content based on the question. A misstep in any of these steps can lead to significant errors in the model’s outputs. For instance, semantic similarity can often lead users to irrelevant materials when using natural language search within research engines.
A fundamental question that arises in the discussion of RAG-based AI tools is how to define hallucinations within the system. Daniel Ho, a Stanford professor, and senior fellow at the institute for Human-Centered AI, highlights the higher rate of mistakes in outputs found through his research compared to what the companies building the models initially found. The concept of hallucinations in a RAG system revolves around whether the output aligns with the model’s data retrieval process. Additionally, the output must be grounded in the provided data and be factually correct, especially in the legal field where complexities are high.
Despite the advancements in RAG-based AI tools for legal professionals, there is still a need for human interaction throughout the process. AI experts stress the necessity of double-checking citations and verifying the overall accuracy of the results. While RAG systems may excel at answering legal questions compared to other AI models, they can still overlook finer details and make random errors. Therefore, users should maintain a sense of skepticism and not entirely rely on the AI tool’s output.
The potential of RAG-based AI tools extends beyond the legal sector and into various professions and industries. Arredondo emphasizes the importance of obtaining answers anchored in real documents, making RAG a staple for professional applications. Risk-averse executives are intrigued by the prospect of leveraging AI tools to understand proprietary data without compromising sensitive information. However, users must be aware of the limitations of these tools and not rely solely on their answers.
Despite the advancements in RAG technology, challenges persist in eliminating hallucinations entirely. Ho acknowledges that there are no foolproof methods to eradicate hallucinations entirely. Human judgment remains paramount, even as RAG systems reduce errors. It is crucial for AI-focused companies to manage user expectations and refrain from overpromising the accuracy of their AI tools. While RAG-based AI tools offer significant benefits, human oversight and skepticism are essential components in ensuring reliable and accurate outputs.