Universal Containers (UC) is experimenting with using public Generative AI models and is familiar with...

UC wants to streamline the use of Generative AI by reducing the time reps spend typing prompts and ensuring consistency, leveraging their existing prompt knowledge. Let’s evaluate the options.

Option A: Agent Builder and Action: Query Records.Agent Builder in Agentforce Studio creates autonomous AI agents with actions like "Query Records" to fetch data. While this could retrieve information, it’s designed for agent-driven workflows, not for simplifying manual prompt entry or ensuring consistency across user inputs. This doesn’t directly address UC’s concerns and is incorrect.

Option B: Einstein Prompt Builder and Prompt Templates.Einstein Prompt Builder, part of Agentforce Studio, allows users to create reusable prompt templates that encapsulate specific instructions and grounding for Generative AI (e.g., using public models via the Atlas Reasoning Engine). UC can predefine prompts based on their known language, saving time for reps by eliminating repetitive typing and ensuring consistency across sales and service teams. Templates can be embedded in flows, Lightning pages, or agent interactions, perfectly addressing UC’s needs. This is the correct answer.

Option C: Einstein Recommendation Builder.Einstein Recommendation Builder generates personalized recommendations (e.g., products, next best actions) using predictive AI, not Generative AI for freeform prompts. It doesn’t support custom prompt creation or address time/consistency issues for reps, making it incorrect.

Why Option B is Correct:

Einstein Prompt Builder’s prompt templates directly tackle UC’s challenges by standardizing prompts and reducing manual effort, leveraging their familiarity with Generative AI language. This is a core feature for such use cases, as per Salesforce documentation.