Which feature of the Einstein Trust Layer helps limit hallucinations and decrease the likelihood of unintended outputs?

The feature that effectively helps limit hallucinations and decrease the likelihood of unintended outputs in the Einstein Trust Layer is Prompt Defense. This approach focuses on refining the prompts given to the AI model, ensuring that the inputs are framed in a way that minimizes the chances of generating erroneous or misleading information. By employing strategies that validate and scrutinize the prompts before they reach the model, Prompt Defense serves to enhance the reliability of the output.

Prompt Defense works by establishing guidelines and boundaries for the kinds of queries the model should respond to, which directly addresses concerns related to hallucinations—instances where the model produces false or fabricated information that seems plausible. This proactive stance in managing inputs leads to a more trustworthy interaction with the AI, resulting in outputs that are not only relevant but also anchored in the context provided.

As for other options, Dynamic Grounding with Secure Data Retrieval focuses on connecting AI outputs with verified data sources, but it does not specifically address the issue of prompt management. Toxicity Scoring is aimed at identifying and mitigating harmful language in outputs, which, while important, does not target the problem of hallucinations directly. Data Masking primarily deals with protecting sensitive information and does not contribute to addressing the accuracy of the information generated by the model.