The correct choice is Prompt engineering. This approach lets the team instruct the model to adopt Aurora Threads' friendly and confident brand voice for every reply without changing model weights or retraining.

By writing explicit style guidance such as a persona statement, tone instructions, and do and do not lists in the system or instruction prompt, Prompt engineering steers the model toward consistent wording and phrasing. It is quick to iterate and low cost because it only changes the input prompts sent to Amazon Bedrock rather than creating a new model artifact.

Few-shot prompting can provide examples that nudge tone but it is often less consistent across varied inputs and it consumes more tokens which increases maintenance and cost.

Temperature parameter tuning changes randomness and can make outputs more conservative or more creative but it does not ensure a specific brand voice or persona.

Model fine-tuning can permanently bake a style into model weights but it requires extra time, cost, and governance and it is unnecessary when clear prompts can reliably impose tone.

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