The negative effect of "persona-based prompting"

A recent study has highlighted how "persona-based prompting", that is, the approach that invites an AI model to imagine itself as an expert in a specific sector, can paradoxically worsen its performance. Although this technique has proven useful in improving the safety of models, it does not seem as effective when it comes to accuracy and reliability of information.

Researchers suggest that the emphasis on assuming a specific "persona" may distract the model from the primary goal of providing accurate and fact-based answers. This phenomenon raises questions about the effectiveness of the most common prompting strategies and the need to refine the interaction techniques with AI models to obtain optimal results.

For those evaluating on-premise deployments, there are trade-offs to consider when optimizing prompts for large models. AI-RADAR offers analytical frameworks on /llm-onpremise to evaluate these aspects.