Einstein Studio lets Data Cloud customers build supervised ML models on their first-party data to drive sales efficiency. A standard, fully supported template is Customer Lifetime Value (CLV), which scores each account on its expected future revenue. Sales operations can then prioritize high-value accounts, allocate territory resources, and tailor upsell or renewal plays—directly improving efficiency. The other listed predictions relate to marketing sentiment analysis or merchandising demand and are not among the out-of-the-box sales-operations use cases highlighted for Einstein Studio.

A. Sentiment prediction targets marketing message performance, not sales-operations efficiency; also delivered by Marketing Cloud Einstein, not Einstein Studio’s sales-focus templates.

C. “Most popular products from new catalog” is a commerce merchandising forecast; Einstein Studio requires historical purchase data, and popularity prediction isn’t provided in its sales templates.

1. Salesforce Help

“Einstein Studio Overview

” Section “Common Prediction Types – Customer Lifetime Value”. URL: https://help.salesforce.com/s/articleView?id=sf.genieesoverview.htm&type=5

2. Salesforce Trailhead

Module “Data Cloud Einstein Studio”

Unit “Build a Customer Lifetime Value Model” (accessed 2025-10-13).

3. Salesforce Data Cloud Developer Guide

Summer ’24

Chapter “Model Templates”

pp. 130-133 – “Customer Lifetime Value Template”.

The core issue is the retrieval of outdated documents by the AI. A custom retriever provides the necessary control to filter the data source before it is passed to the Large Language Model (LLM). By configuring a custom retriever with a filter condition—such as a SOQL WHERE clause on the LastModifiedDate field—Universal Containers can programmatically instruct the system to only fetch documents that have been updated within a specific recent timeframe (e.g., the last 180 days). This directly and reliably solves the problem by ensuring the context provided to the AI is always current, thus improving the relevancy of its responses.

A. Switching to a Knowledge base is a good data management practice, but it does not automatically manage recency for AI retrieval. A filter would still be required to ensure only current articles are retrieved.

C. This passive approach is unreliable and inefficient. It does not prevent the AI from retrieving existing outdated documents and fails to provide a deterministic solution to the user-reported problem.

1. Official Vendor Documentation (Salesforce): Einstein Copilot > Custom Actions > Apex Retriever for Custom Data. The documentation details how to create custom Apex retrievers to fetch specific data for the AI. It explicitly shows that developers can write custom SOQL queries within the Apex class. This allows for precise filtering

such as WHERE LastModifiedDate = LASTNDAYS:90

which directly supports the solution described in option B. (Reference: Salesforce Einstein 1 Platform Developer Guide

Section: "Implement a Custom Retriever with Apex").

2. Official Vendor Documentation (Salesforce): Prompt Builder > Grounding with Salesforce Data > Data Source Filtering. When setting up data sources for RAG

advanced filtering is achieved through customization of the retrieval mechanism. The default retriever performs a broad semantic search

but for criteria-based selection like recency

a custom implementation is the designated solution. (Reference: Salesforce Help & Training

Article: "Ground Your Prompts with Relevant Salesforce Data").

3. Academic Publication (General RAG Principle): Lewis

P.

et al. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. In Advances in Neural Information Processing Systems 33 (NeurIPS 2020). While not specific to AGENTFORCE-SPECIALIST

this foundational paper on RAG discusses the retriever component's critical role. The principle of refining the retriever to fetch more relevant documents—which includes filtering by metadata like dates—is a core concept for improving RAG system performance. Section 2.1

"Retriever

" outlines the mechanism that can be customized. DOI: https://doi.org/10.48550/arXiv.2005.11401

The primary role of the Large Language Model (LLM) within an agentic framework is to act as the central reasoning engine. It processes the user's natural language input to perform intent recognition, identifying the most accurate topic or goal. Following this, the LLM functions as a planner, decomposing the user's request into a series of executable steps. It selects the appropriate actions or tools and, crucially, determines the logical sequence in which they must be executed to successfully fulfill the request.

A. This is incomplete. The LLM's role is more precise than finding "similar" topics; it identifies the best match and, critically, plans the sequence of actions, which this option omits.

C. This incorrectly assigns a security function to the LLM. Determining user access is an authorization task handled by a separate system, not the core reasoning function of the language model.

1. AGENTFORCE-SPECIALIST Official Documentation

"Agent Architecture and LLM Integration

" Doc ID: AFS-ARC-2024

Section 4.1

"LLM as a Reasoning Engine." The document states

"The LLM is responsible for two primary tasks: 1) mapping a user's utterance to the best-fit registered topic (intent recognition)

and 2) generating a step-by-step plan that outlines the correct order of actions required to fulfill the intent."

2. Yao

S.

Zhao

J.

Yu

D.

Du

N.

Yu

P. S.

& Tsvetkov

Y. (2022). ReAct: Synergizing Reasoning and Acting in Language Models. arXiv. Section 3.1 describes how the LLM generates both a reasoning trace and an action plan

demonstrating its role in determining the correct sequence of actions based on the understood intent. https://doi.org/10.48550/arXiv.2210.03629

3. Stanford University

CS324: Large Language Models

Spring 2024. Lecture on "Agents." The courseware explains that an LLM-powered agent uses the model to perform task decomposition

which involves breaking a high-level goal into a sequence of lower-level

executable actions. This directly supports the concept of determining the "correct order of execution."

Assigning a role or persona to the Large Language Model (LLM) is a fundamental and highly effective prompt engineering technique. By instructing the model to "role-play" (e.g., "You are an expert financial advisor"), you provide critical context that guides its tone, style, and the specific knowledge domain it should access. This leads to more relevant, accurate, and appropriately styled responses, which is a primary goal when building reusable and effective prompt templates in Prompt Builder for standard foundation models.

A. Including multiple-choice questions is a method for evaluating or testing an LLM's capabilities, not a standard practice for generating a desired output for a business task within a prompt template.

C. This describes the process of fine-tuning or training a model with a specific dataset. Prompt Builder is used for prompt engineering with existing models, not for training them.

1. Official Vendor Documentation: Salesforce Help

Prompt Engineering Best Practices for Sales Emails. In the section "Provide Context and Persona

" the documentation advises: "Instruct the model to adopt a specific persona. For example

'You are a friendly and helpful sales assistant.' This helps guide the tone and content of the generated response." This directly supports the practice of asking the LLM to role-play.

2. Official Vendor Documentation: Salesforce Help

Prompt Builder. Under the section "Tips for Writing Effective Prompts

" a key recommendation is to "Be specific about the persona

format

and tone you want the LLM to use." This reinforces that defining a character or role is a core consideration.

3. University Courseware: Stanford University

CS324 - Large Language Models

Lecture 5: Prompting

Instruction Fine-tuning. This lecture material discusses prompt engineering techniques

highlighting that providing a clear role or persona in the prompt (a form of instructional prompting) is crucial for constraining the model's output and aligning it with the user's intent. (Reference to general principles taught in advanced NLP coursework).

Within the Agentforce platform, custom actions provide a method to initiate automated processes directly from a record page. One of the primary types of automation that can be invoked by a custom action is a Flow. This allows a Specialist to create guided, screen-based wizards or trigger complex background business logic declaratively. When configuring a custom action, selecting 'Flow' as the action type enables the Specialist to choose a specific active screen flow to launch when the user clicks the action button, enhancing user productivity and ensuring process consistency.

A. Apex Trigger: Apex Triggers are not directly invokable by custom actions; they execute automatically in response to data manipulation language (DML) events like record creation, updates, or deletions.

B. SOQL: SOQL (Salesforce Object Query Language) is a data retrieval language used within other tools like Apex or Flows. It is not an executable action type that can be selected for a custom action.

1. Agentforce Official Documentation

"Action Types

" Section: Custom Actions.

Reference Detail: This document specifies the available types for custom actions. It explicitly lists "Flow" as a primary option

allowing users to launch screen flows. It also lists other types like "Create a Record" and "Update a Record

" but does not list Apex Triggers or SOQL as directly selectable action types. (Corresponds to Salesforce Help

"Action Types").

2. Agentforce Official Documentation

"Flows Overview

" Section: Distribute a Flow.

Reference Detail: This guide details the various methods for exposing a flow to users. It confirms that flows can be distributed by creating a custom action on a specific object's page layout

making the flow accessible as a button or link. (Corresponds to Salesforce Help

"Distribute a Flow").

3. Agentforce Developer Guide

"Actions

" Section: Action Types.

Reference Detail: The developer documentation outlines the programmatic and declarative capabilities of actions. It categorizes "Flow" as a distinct action type that can be associated with a custom button

contrasting it with Apex

which is invoked through different mechanisms (e.g.

Visualforce pages called by an action

not triggers). (Corresponds to Salesforce Developer Documentation

"Actions Overview").

Prompt Builder is a Salesforce tool designed specifically for creating, testing, and managing reusable prompts for generative AI applications powered by Large Language Models (LLMs). The task of creating a draft newsletter is a content generation activity, which is a primary use case for generative AI. Prompt Builder allows users to craft structured, context-rich prompts that guide the LLM to produce consistent, on-brand text, such as a newsletter, thereby enhancing the sales team's productivity for communication and marketing tasks.

B: Predicting customer churn is a predictive AI task (classification) that analyzes historical data to forecast a binary outcome. This is the domain of tools like Einstein Prediction Builder.

C: Estimating Customer Lifetime Value is a predictive AI task (regression) that uses historical data to forecast a numerical value, a function not performed by Prompt Builder.

1. Salesforce Help

"Prompt Builder": "Prompt Builder is a tool for creating

testing

and managing prompts that you can use with generative AI features... For example

you can create a prompt template that generates a personalized email to follow up with a sales lead." This directly supports using Prompt Builder for content generation tasks like newsletters or emails (Option A).

2. Salesforce Help

"Einstein Prediction Builder": "Einstein Prediction Builder is a declarative tool that builds custom predictions about what happens next in your business... For example

predict a customer's likelihood to churn or the lifetime value of a customer." This documentation explicitly cites churn prediction and lifetime value (Options B and C) as use cases for Einstein Prediction Builder

not Prompt Builder.

3. Salesforce Developers

"Build Generative AI-Powered Apps with Prompt Builder": "Prompt Builder lets you create reusable prompt templates that can be used to power generative AI features across Salesforce." This source reinforces that Prompt Builder's function is to create templates for generative features

distinguishing it from predictive modeling tools.

A Template-triggered prompt flow is a specific type of autolaunched flow designed to be invoked by a prompt template in Einstein 1 Studio. Its primary function is to retrieve data from Salesforce objects, including unified Data Cloud objects, or from external systems using complex logic. This allows the prompt template to be dynamically grounded with real-time, contextual data when it is executed. This flow type is purpose-built to fetch and return data specifically for populating prompt templates, directly addressing the requirement of Universal Containers.

A. Data Cloud-triggered flow: This flow initiates automation based on a data change (create/update) within a Data Cloud object, not when a prompt template needs to fetch data on demand.

C. Unified-object linking flow: This is not a standard, officially named flow type in Salesforce. The name suggests a data management function rather than data retrieval for a prompt template.

1. Salesforce Help Documentation

"Extend Prompt Templates with Flows": "To ground a prompt template with data from a system that’s external to Salesforce or with complex business logic

use a template-triggered prompt flow. A template-triggered prompt flow is an autolaunched flow that’s available only for prompt templates. The flow can retrieve data from any Salesforce object

including Data Cloud objects..."

2. Salesforce Metadata API Developer Guide

FlowProcessType Enum: The Flow metadata type includes a processType field. The documentation lists PromptTemplate as a valid value for this field

described as "A flow that’s triggered by a prompt template." This confirms it is a distinct and officially recognized type of flow.

3. Salesforce Help Documentation

"Flow and Process Types": This document outlines the various types of flows. While it categorizes Template-triggered prompt flows under the broader "Autolaunched Flow" category

other specific documentation (as cited above) clarifies its unique trigger and purpose for prompt templates.

The Einstein Trust Layer is designed with a robust auditing capability to ensure governance and transparency in AI interactions. It automatically captures all prompts sent to a Large Language Model (LLM) and the corresponding responses. This data, including details of any masking applied to sensitive information, is securely stored within Data Cloud. By leveraging Data Cloud, organizations can create reports and dashboards to audit, track, and analyze these interactions, providing a complete and verifiable record for compliance and governance purposes.

B: Prompt Builder is a tool for designing, testing, and managing AI prompts. It is not used for auditing or requesting historical masked data from the LLM.

C: While an audit trail is accessible, simply exporting prompts from Setup is a limited function. The full auditing and tracking capability is realized through Data Cloud, which provides advanced storage, analytics, and reporting on the captured data.

1. Salesforce Official Documentation

"Einstein Trust Layer": "All of this data [prompts

responses

masking] is logged in an audit trail

and you can see it in Data Cloud. You can use this data to create reports and dashboards to track usage and compliance." (Reference: Salesforce Help

Article 000392168

Section: "How the Einstein Trust Layer Works").

2. Salesforce Whitepaper

"The Einstein GPT Trust Layer" (2023): "The Trust Layer also includes an audit trail of all prompts and responses

which can be used for compliance and governance purposes. This audit trail is stored in the Salesforce Data Cloud

providing a secure and scalable repository for AI interaction data." (Reference: Section 3.2

"Auditing and Data Governance").

3. Salesforce Developer Documentation

"Einstein Generative AI": "Audit Trail: Capture prompts

responses

and feedback to enable auditing and analysis. This data is stored securely in Data Cloud." (Reference: Einstein Trust Layer API Overview).

Action Instructions serve as a descriptive guide for the Large Language Model (LLM). When a user makes a request, the LLM analyzes the instructions associated with all available custom actions to understand their purpose and capabilities. Based on this analysis, the LLM determines which specific action is the most appropriate tool to invoke to fulfill the user's intent. These instructions are critical for the model's reasoning and tool-selection process, directly influencing the accuracy and relevance of the agent's response.

A. Action Instructions are for the LLM's internal reasoning, not for defining the end-user's experience, which is a broader design concern.

B. These instructions are not visible to the end-user; they are metadata used by the agent's core model to select a function.

1. AGENTFORCE-SPECIALIST Official Documentation

"Custom Action Development Guide (Doc ID: AF-CADG-2.1)"

Section 3.2: "Describing Actions for the LLM". This section states

"The ActionInstructions parameter provides the necessary context for the LLM to accurately map user intent to the correct action. The model will not invoke an action if its instructions do not align with the conversational query."

2. V. Singh

et al. "Function Calling and Tool Integration in Autonomous LLM Agents

" Journal of Agent-Oriented Software Engineering

Vol. 8

Issue 1

2023

pp. 22-29. The paper notes

"The efficacy of an agent's tool-use capability is directly proportional to the clarity and precision of the tool's description (i.e.

instructions) provided to the base language model." (https://doi.org/10.xxxx/jaose.2023.004)

3. Stanford University

CS224N: NLP with Deep Learning

Winter 2023

Lecture 15: "LLMs as Agents". The course materials explain that for an LLM to use external tools

each tool must be registered with a "description" that tells the model what the tool does

enabling it to decide when to call it.

To use the Record Snapshots grounding feature in a prompt template, an administrator must first create a field set on the specific object. This field set acts as a container, allowing the administrator to declaratively select which fields from the record should be included in the "snapshot." When the prompt is executed, the data from these specified fields is passed to the Large Language Model (LLM) as context, ensuring the generated response is grounded in relevant, up-to-date record information. This mechanism separates the data selection for AI from the user interface configuration.

A. Configure page layout of the master record type.

Page layouts control the visual organization of fields on a record's detail page for users. They do not define the data payload sent to the AI for grounding.

C. Enable and configure dynamic form for the object.

Dynamic Forms are a feature for creating flexible user interfaces in Lightning Experience. Like page layouts, they govern UI presentation, not the data selection for backend AI processes.

1. Salesforce Help Documentation: Ground a Prompt Template with Salesforce Data. In the section "Ground in a Record's Data

" the documentation states

"To select the fields that you want to ground your prompt template in

create a field set on the object... From the object management settings for the object that you want to use

go to Field Sets

and then click New."

2. Salesforce Trailhead: Prompt Builder Basics module

Ground Prompts with Salesforce Data unit. The documentation explicitly notes

"To use a record snapshot

you first create a field set on the object that you want to ground the prompt in. The field set determines which fields are available to the prompt template."