AI-201 · Question #237
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…
The correct answer is B. Use well-defined output instructions and provide desired output examples. Prompt engineering best practice for structured extraction is to combine clear output format instructions with few-shot examples (showing the LLM exactly what the desired output looks like). Examples are especially powerful because they anchor the model's response format…
Question
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?
Options
- AAsk the LLM to extract and only output the important information in the text.
- BUse well-defined output instructions and provide desired output examples.
- CEnsure in the prompt that the LLM has been told to only use name value pairs in the response.
How the community answered
(27 responses)- A11% (3)
- B81% (22)
- C7% (2)
Explanation
Prompt engineering best practice for structured extraction is to combine clear output format instructions with few-shot examples (showing the LLM exactly what the desired output looks like). Examples are especially powerful because they anchor the model's response format precisely, reducing hallucination and conversational filler. Option A ('only output the important information') is too vague and the LLM may still add preamble. Option C (forcing name-value pairs) is overly prescriptive about format but doesn't address preventing extra text as effectively as pairing instructions with examples.
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