Exam Agentforce-Specialist Topic 3 Question 263 Discussion

Actual exam question for Salesforce's Agentforce-Specialist exam
Question #: 263
Topic #: 3
Choose 1 option.
An Agentforce Specialist needs to create a prompt template that extracts the customer's name, phone number, and case number from a block of text, and nothing else.
How should the Agentforce Specialist structure the prompt to ensure the large language model (LLM) doesn't include extra conversation or text?

Suggested Answer: B Vote an answer

According to the official AgentForce Prompt Template Design Guide, when extracting specific data such as customer name, phone number, and case number from unstructured text, the best practice is to use well- defined output instructions and examples. The documentation specifies: "To ensure the LLM produces consistent and precise outputs, prompts must include explicit output formatting instructions and examples that demonstrate the desired structure." AgentForce guidance emphasizes structured output control to prevent the LLM from adding conversational or extraneous text. It states: "Always define your output schema clearly - for example, specify JSON or key- value pairs - and provide one or more examples of what the model should return. This ensures the model responds only with structured data and not natural language." Option A ("Ask the LLM to extract and only output important information") is too vague and can still produce variable or verbose responses. Option C ("Ensure the LLM has been told to only use name value pairs") is partially correct but incomplete without clear formatting and example output. Therefore, Option B is the correct choice as it aligns with AgentForce's documented standards for prompt accuracy and reliability.
References (AgentForce Documents / Study Guide):
AgentForce Prompt Engineering Best Practices Guide
AgentForce Developer Study Guide: "Defining Structured Outputs in Prompt Templates" AgentForce Technical Documentation: "Using Output Instructions and Examples for LLM Control"

by Justin at Jul 22, 2026, 05:01 AM

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