The correct option is Define explicit safety constraints and guardrails within the prompt. This approach embeds the credit union's policy rules directly into the prompt so the model will produce recommendations that stay within approved guidance.

Encoding constraints, required formats, and example-safe responses in the prompt gives the model clear operational boundaries and practical checks it must follow. You can include forbidden actions, required disclosures, and example interactions that demonstrate compliant behavior and you can ask the model to justify how a recommendation satisfies policy so outputs are more auditable.

Use zero-shot prompting to elicit more direct answers is incorrect because zero-shot prompts do not supply the explicit rules or examples needed to constrain behavior and they tend to increase output variability when strict compliance is required.

Increase the model's input and output token limits is incorrect because changing token limits only affects how much context the model can process and how long responses can be and it does not impose safety constraints or change the model's adherence to policy.

Amazon GuardDuty is incorrect because GuardDuty is an AWS threat detection service for accounts and workloads and it is not a prompt-engineering or model-steering tool, so it will not enforce compliance in model outputs.

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