Quantifying GenAI Confidence in Customer Support: Judge LLMs and Automated Scoring Loops
Failed to add items
Add to basket failed.
Add to wishlist failed.
Remove from wishlist failed.
Adding to library failed
Follow podcast failed
Unfollow podcast failed
Quantifying GenAI Confidence in Customer Support: Judge LLMs and Automated Scoring Loops
-
Narrated by:
-
By:
About this listen
In this episode, we explore how the SupportLogic Engineering Team is transforming generative AI summarization from a risky, black-box experiment into a trustworthy, enterprise-grade system. Moving GenAI into real-world production requires more than just a good underlying model—it demands measurable confidence. We break down SupportLogic's innovative evaluation framework, which relies on "Judge LLMs" to automatically assess AI-generated summaries across six critical dimensions: faithfulness, instruction adherence, hallucination risk, topic coverage, clarity, and persona usability.
Listen in as we discuss how this continuous, automated scoring loop enables data-driven prompt tuning and dynamic model routing. We also dive into their latest benchmark data, comparing the quality and cost-efficiency of top-tier models like Claude 4 Sonnet, Gemini 1.5 Pro, and GPT-4o Mini. Whether you are balancing high-stakes accuracy with latency-sensitive workflows or simply trying to eliminate hallucinations in customer-facing summaries, this episode provides a strategic roadmap for deploying GenAI with quantifiable, reliable results.