When using a prompt template, what should an Agentforce Specialist consider with their grounding data and chosen model?

Question

Options

• AReview the token limit in the Einstein Trust Layer.
• BEnsure queries used for grounding employ offset so the token limits of models are not exceeded.
• CReview the model limitation in Prompt Builder versus the grounding data size.

• A
  3% (1)
• B
  3% (1)
• C
  94% (29)

Explanation

The most critical technical consideration when pairing a prompt template's grounding data with a chosen Large Language Model (LLM) is the relationship between the two. The correct action is to review the model limitation in Prompt Builder versus the grounding data size (C). Every LLM has a fixed context window limit, typically expressed in tokens (the model's units for processing text). This token limit defines the maximum amount of input data (the prompt template text + all the dynamic grounding data) and output data the model can handle in a single request. The grounding data, which is pulled dynamically from Salesforce records (e.g., related lists, long text fields, Flow outputs), varies significantly in size from one record to the next. If the combined size of the prompt and the dynamic data for a specific record exceeds the LLM's token limit, the generative AI request will fail with a "token limit exceeded" error. The Agentforce Specialist must proactively design the template to limit the amount of data retrieved (e.g., using Flow to summarize related lists or querying only essential fields) to ensure it stays within the chosen model's capacity.

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