Universal Containers aims to streamline the sales team's daily tasks by using AI. When considering these new workflows, which improvement requires the use of Prompt Builder?

Prompt Builder is a Salesforce tool designed for creating and managing reusable prompts for generative AI. Its primary function is to take contextual data from Salesforce records and use it to generate new content, such as text summaries or draft communications. Populating an AI-generated summary for a sales contract is a classic generative AI task where a Large Language Model (LLM) synthesizes information from the contract's text into a concise summary. This directly aligns with the core capability of Prompt Builder to create templates for such generative actions.

A. This is a predictive AI task. Estimating a future numerical value like "time-to-close" based on historical data is a function of Einstein Prediction Builder, not the generative Prompt Builder.
C. This is a specific predictive AI application. Einstein Lead Scoring uses machine learning to analyze lead data and historical conversion patterns to assign a numerical score, which is a predictive, not generative, function.

1. Salesforce Help, "Prompt Builder": "With Prompt Builder, you can create, test, customize, and manage prompt templates that use your CRM data... For example, you can create a prompt template that your sales team can use to generate a personalized email to a contact... or summarize a complex case." This document establishes that summarization is a key use case for Prompt Builder.

2. Salesforce Help, "Einstein Prediction Builder": "Einstein Prediction Builder is a declarative tool that lets you build custom predictions on your Salesforce data with just a few clicks... For example, you can build a prediction to determine the likelihood that a customer will pay their invoice on time." This source confirms that predicting numerical outcomes or likelihoods is the domain of Prediction Builder.

3. Salesforce Help, "How Einstein Lead Scoring Works": "Einstein Lead Scoring uses data science and machine learning to discover the patterns of lead conversion in your business, and predict which leads are most likely to convert." This clearly defines Lead Scoring as a predictive, rather than generative, AI feature.