Announcing Reinforcement Learning on the Indemn Platform
Delivering precise and personalized AI models through human feedback
The rapid evolution of Large Language Models (LLMs) has opened up a world of possibilities for insurance agents and carriers. However, two key challenges have emerged: personalization and response accuracy. How can insurance businesses ensure their AI communicates in their unique voice while providing helpful, accurate information to customers? Our top-down approach to building AI agents has yielded significant success, but we recognized the potential for even greater customization and accuracy.
Introducing RLHF: A bottom-up solution
Recently, we’ve enhanced AI model customization by introducing Reinforcement Learning through Human Feedback (RLHF). All Indemn Platform customers can now access this innovative feature. While RLHF isn’t new in AI, our implementation is unique and empowering for sales and service conversations, capturing real-time feedback to continually improve AI responses. The Platform allows any team member to annotate outgoing responses—whether from an AI agent or a CSR using the Agent Copilot—with quick comments. These annotations highlight issues or suggest better responses, creating a feedback loop that refines prompts, the knowledge base, and key functions.
How is it different?
Our philosophy at Indemn is to use technology to reduce work for insurance businesses while being mindful of the new tasks technology can introduce. This philosophy shapes our RLHF strategy in two key ways. CSRs can see annotations made by others, preventing duplicate reports and leveraging collective wisdom. The immediate feedback process enables an AI model to improve itself daily, take more of the mundane work off of team members, and enable immediate access to information for customers and partners.
The future of AI customization at Indemn
We’re not stopping here. Our vision for the future includes building a comprehensive suite of AI model customization tools. Eventually, our reinforcement learning process will build foundation models from the ground up for each customer. As LLMs improve in interpreting qualitative feedback, we’ll incorporate intelligence that uses crowdsourced suggestions to enhance AI responses.
Our approach moves beyond simple thumbs-up/thumbs-down methods, treating annotations as instructions for more nuanced improvements. By implementing RLHF, we’re making our AI agents more accurate and aligned with the unique voice of each business. This not only enhances customer interactions but also improves overall satisfaction and efficiency.
As we continue to innovate, we remain committed to our goal: empowering insurance professionals with cutting-edge AI tools that enhance rather than replace human expertise. To learn more about how Indemn’s RLHF implementation can benefit your insurance business, contact us today at support@indemn.ai.