Input Guardrails

Data Quality

Our AI Sales Assistant depends on the quality of data fed to it. We ensure accurate and up-to-date leads and engagement data to prevent misinformed interactions.

Contextual Understanding:

To enhance the AI Sales Assistant’s ability to handle inputs responsibly, we use advanced contextual understanding techniques. This helps the AI to interpret user queries correctly and avoid misunderstandings that could lead to inappropriate or incorrect responses.

Output Guardrails

Quality Assurance and Refinement:

The AI’s responses are continuously monitored and evaluated for quality and relevance. We employ unit testing and leverage LLMs as judges to assess the coherence, accuracy, and appropriateness of the outputs. Responses that do not meet our quality standards are refined, which ensures that the final output is well-formatted, contextually accurate, and aligned with the desired tone and intent.

Personalization and Sensitivity:

To enhance user experience and ensure sensitive handling of personal information, the AI is designed to personalize responses based on user-specific data, while maintaining strict confidentiality and data protection measures. The assistant’s output is tailored to meet individual user needs and preferences, ensuring that responses are relevant and considerate of the user’s context and requirements.

Feedback and Iterative Improvement:

We encourage user feedback on all responses to identify areas for improvement. This feedback loop helps us fine-tune the AI’s output and address any issues related to accuracy, relevance, or tone. Updates are made based on user feedback and performance assessments, ensuring that the AI continuously evolves to provide better, more reliable responses.

At this point, we haven’t instituted Llama Guard but we plan to do so in the near future.