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Universal Containers (UC) recently rolled out Einstein Generative capabilities and has created a custom prompt to summarize case records. Users have reported that the case summaries generated are not returning the appropriate information. What is a possible explanation for the poor prompt performance?
Correct Answer: A
Poor prompt performance when generating case summaries is often due to the data used forgroundingbeing incorrect or incomplete. Grounding involves feeding accurate, relevant data to the AI so it can generate appropriate outputs. If the data source is incomplete or contains errors, the generated summaries will reflect that by being inaccurate or insufficient. * Option B(prompt template incompatibility with the LLM) is unlikely because such incompatibility usually results in more technical failures, not poor content quality. * Option C(Einstein Trust Layer misconfiguration) is focused on data security and auditing, not the quality of prompt responses. For more information, refer toSalesforce documentation on grounding AI modelsand data quality best practices.
