A company built a chatbot with a chosen foundation model and needs the bot to answer technical questions in the company’s specific tone without human help. Which approach will help the model produce responses that match the company’s tone?

Iteratively refining and testing the prompt is the most effective way to guide a foundation model to adopt a specific tone. By adjusting the prompt with explicit instructions (e.g., "Respond in a formal, helpful, and concise manner"), observing the model's output, and making further adjustments, you can fine-tune its behavior without retraining. Limiting tokens would restrict response length, not tone. Batch inferencing is for processing multiple inputs efficiently, not for tone control. Increasing the temperature parameter makes responses more creative and less predictable, which is counterproductive when aiming for a consistent, specific tone.