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Universal Containers (UC) plans to automatically populate the Description field on the Account object.
Which type of prompt template should UC use?
Context of the Question Universal Containers (UC) wants to automatically populate the Description field on the Account object. The AI-driven solution must generate textual data and write it directly into a field.
Field Generation Prompt Template
Primary Use Case: A Field Generation prompt template is specifically designed to create or fill in fields on a record with AI-generated text.
Auto-population: By configuring a Field Generation prompt template, admins can define the instructions, data inputs, and desired output for the AI. The resulting text then populates the specified field, such as the Account Description.
Why Not Flex or Sales Email Prompt Templates?
Flex Prompt Template: Used to combine or manipulate data across objects, merges, or references from multiple sources in more advanced, flexible prompts. Typically not the go-to for straightforward text generation on a single field.
Sales Email Prompt Template: Focused on drafting or summarizing emails for sales reps (like crafting outreach or follow-up messages). This template is not specifically built to populate a field on a record.
Conclusion For automatically populating the Description field with AI-generated content, the Field Generation prompt template (Option A) is the correct choice.
Salesforce AI Specialist Reference & Documents
Salesforce AI Specialist Study Guide Highlights Field Generation prompt templates for populating or updating record fields with AI-generated text.
What is best practice when refining Einstein Copilot custom action instructions?
When refining Einstein Copilot custom action instructions, it is considered best practice to provide examples of user messages that are expected to trigger the action. This helps ensure that the custom action understands a variety of user inputs and can effectively respond to the intent behind the messages.
Option B (consistent phrases) can improve clarity but does not directly refine the triggering logic.
Option C (specifying a persona) is not as crucial as giving examples that illustrate how users will interact with the custom action.
For more details, refer to Salesforce's Einstein Copilot documentation on building and refining custom actions.
What is the role of the large language model (LLM) in executing an Einstein Copilot Action?
In Einstein Copilot, the role of the Large Language Model (LLM) is to analyze user inputs and identify the best matching actions that need to be executed. It uses natural language understanding to break down the user's request and determine the correct sequence of actions that should be performed.
By doing so, the LLM ensures that the tasks and actions executed are contextually relevant and are performed in the proper order. This process provides a seamless, AI-enhanced experience for users by matching their requests to predefined Salesforce actions or flows.
The other options are incorrect because:
A mentions finding similar requests, which is not the primary role of the LLM in this context.
C focuses on access and sorting by priority, which is handled more by security models and governance than by the LLM.
Salesforce Einstein Documentation on Einstein Copilot Actions
Salesforce AI Documentation on Large Language Models
An AI Specialist built a Field Generation prompt template that worked for many records, but users are reporting random failures with token limit errors.
What is the cause of the random nature of this error?
The reason behind the token limit errors lies in the dynamic nature of the prompt template used in Field Generation. In Salesforce's AI generative models, each prompt and its corresponding output are subject to a token limit, which encompasses both the input and output of the large language model (LLM). Since the prompt template dynamically adjusts based on the specific data of each record, the number of tokens varies per record. Some records may generate longer outputs based on their data attributes, pushing the token count beyond the allowable limit for the LLM, resulting in token limit errors.
This behavior explains why users experience random failures---it is dependent on the specific data used in each case. For certain records, the combined input and output may fall within the token limit, while for others, it may exceed it. This variation is intrinsic to how dynamic templates interact with large language models.
Salesforce provides guidance in their documentation, stating that prompt template design should take into account token limits and suggests testing with varied records to avoid such random errors. It does not mention switching to Flex template type as a solution, nor does it suggest that token limits fluctuate with user demand. Token limits are a constant defined by the model itself, independent of external user load.
Salesforce Developer Documentation on Token Limits for Generative AI Models
Salesforce AI Best Practices on Prompt Design (Trailhead or Salesforce blog resources)
An AI Specialist needs to create a Sales Email with a custom prompt template. They need to ground on the following data.
Opportunity Products Events near the customer Tone and voice examples
How should the AI Specialist obtain related items?
To ground a sales email on Opportunity Products, Events near the customer, and Tone and voice examples, the AI Specialist should use a prompt-initiated flow. This flow can dynamically fetch the necessary data from related records in Salesforce and ground the generative AI output with contextually accurate information.
Option B (flex template) does not provide the ability to fetch dynamic data from Salesforce records automatically.
Option C (manual insertion) would not allow for the dynamic and automated grounding of data required for custom prompts.
Refer to Salesforce documentation on flows and grounding for more details on integrating data into custom prompt templates.
