To resolve mismatches in generated AI summaries, the most effective strategy is to refine the core prompt design. Option B outlines the fundamental principles of prompt engineering. Creating concise, clear templates with effective grounding (using merge fields to pull data from the Guest object) is critical to ensure the AI bases its summary on the correct, specific record data, directly addressing the issue of "mismatches." Furthermore, providing contextual role-playing and clear instructions guides the Large Language Model (LLM) to produce outputs that are consistent in tone and format, while iterative feedback is the process of refining these elements until the desired accuracy is achieved.

A. This describes an A/B testing methodology. While useful for comparing well-designed prompts, it is not the primary strategy for fixing foundational design flaws that cause data mismatches.

C. Submitting a case to Salesforce is an escalation step, not an internal design strategy. While using the playground for testing is correct, this option lacks the core principles of how to refine the prompt.

1. Salesforce Help

Einstein Generative AI

"Prompt Template Best Practices": This official documentation directly supports the principles in option B. It states

"Ground your prompt in Salesforce data by using merge fields... Give the LLM clear instructions... Tell the LLM what role to play... Test and refine your prompt templates." These points directly correspond to grounding

clear instructions

role-playing

and iterative feedback.

2. Salesforce Trailhead

"Prompt Builder Basics" module

"Create a Prompt Template" unit: This educational resource emphasizes the importance of providing specific instructions and using merge fields to ground the prompt in contextual Salesforce data. It notes

"To ground a prompt template in your Salesforce data

you add merge fields... Clear instructions are key to getting a useful response." This validates the core components of the correct answer.

3. Wei

J.

et al. (2022). Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. Cornell University. arXiv:2201.11903. While a broader academic paper

it underpins the principle of providing clear

structured instructions (a key part of option B) to guide the model's output

which is essential for improving accuracy and reducing "mismatches." (Section 2

"Chain-of-Thought Prompting").