Helping Hand Questions for Salesforce-AI-Specialist

Instructions:This is a core component of Custom Copilot Actions. Instructions tell the AI model what the action should do and how it should be executed. Clear and concise instructions are crucial for the action to function correctly and provide the expected outcome.

Let's look at why the other options are not the primary core component:

Output Types:While important for defining the kind of data the action produces, it's not the core defining element of the action itself.

Action Triggers:These determine when the action is initiated, but they don't define the core functionality of the action.

Prompt Execute Template permission set:This permission set is specifically designed to allow users to execute existing prompt templates. This is exactly what Universal Containers needs for its sales reps.

Prompt Template user permission set:This permission set likely grants broader access, including potentially creating or modifying templates, which is not required in this scenario.

Prompt Template Manager permission set:This permission set likely grants even more extensive administrative access to prompt templates, going beyond what the sales reps need.

By granting sales reps the "Prompt Execute Template" permission set, you ensure they have the necessary access to use prompt templates without granting unnecessary permissions that could potentially lead to unintended changes or security risks.

When buildingprompt templates in Prompt Builder, it is essential to consider how the Large Language Model (LLM) processes and generates outputs. Training the LLM with variouswriting styles, such as differentword choices, intensifiers, emojis, and punctuation, helps the model better understand diverse writing patterns and produce more contextually appropriate responses.

This approach enhances the flexibility and accuracy of the LLM when generating outputs for different use cases, as it is trained to recognize various writing conventions and styles. The prompt template should focus on providing rich context, and this stylistic variety helps improve the model’s adaptability.

Options A and B are less relevant because adding multiple-choice questions or role-playing scenarios doesn’t contribute significantly to improving the AI’s output generation quality within standard business contexts.

For more details, refer to Salesforce’sPrompt Builder documentationand LLM tuning strategies.

When a custom Agent action is not being prioritized correctly by the planner service, the root cause is often missing or improperly defined action dependencies. The planner service determines the execution order of actions based on dependencies defined in the action instructions. To resolve this, the AISpecialist must explicitly specify dependent actions using their API names in the custom action’s configuration. This ensures the planner understands the sequence in which actions must be executed to meet business logic requirements.

Salesforce documentation highlights that dependencies are critical for orchestrating workflows in Einstein Bots and Agentforce. For example, if Action B requires data from Action A, Action A’s API name must be listed as a dependency in Action B’s instructions. The Einstein Bot Developer Guide states that failing to define dependencies can lead to race conditions or incorrect execution order.

In contrast:

Profiles or custom permissions (B) control access to the action but do not influence execution order.

LLM model provider and version (C) determine the AI model used for processing but are unrelated to the planner’s sequencing logic.