AI-201 · Question #24
Universal Containers (UC) recently rolled out Einstein Generative AI capabilities and has created a custom prompt to summarize case records. Users have reported that the case summaries generated are n
The correct answer is B. The data being used for grounding is incorrect or incomplete.. Grounding is the technique of providing relevant, contextual data to the LLM so it can generate accurate, specific outputs. If the case summaries are returning inappropriate or irrelevant information, the most likely root cause is that the grounding data - the fields, related rec
Question
Universal Containers (UC) recently rolled out Einstein Generative AI 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?
Options
- AThe prompt template version is incompatible with the chosen LLM.
- BThe data being used for grounding is incorrect or incomplete.
- CThe Einstein Trust Layer is incorrectly configured.
How the community answered
(57 responses)- A18% (10)
- B74% (42)
- C9% (5)
Explanation
Grounding is the technique of providing relevant, contextual data to the LLM so it can generate accurate, specific outputs. If the case summaries are returning inappropriate or irrelevant information, the most likely root cause is that the grounding data - the fields, related records, or data sources feeding the prompt - is incorrect, incomplete, or poorly mapped. The LLM itself may be functioning correctly, but without accurate input data, it cannot produce accurate outputs ('garbage in, garbage out'). LLM version incompatibility (A) would typically cause errors, not poor content, and the Trust Layer (C) handles security/masking, not content quality.
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