📋 Coming Soon — This article is in the content pipeline and will be published shortly.
Overview
AI-powered call quality evaluation is fundamentally an LLM engineering problem — and it's a hard one. Unlike text summarization or sentiment analysis in isolation, call evaluation must produce consistent, trustworthy scores that real businesses use to measure team performance, enforce compliance, and improve customer outcomes.
Topics Covered
- What "call quality" means for AI systems vs. traditional human QA scorecards
- Designing evaluation criteria: sentiment, resolution, compliance, tone, policy adherence
- LLM-as-judge architectures: how and when to use an LLM to score another LLM's output
- Building a reliable evaluation pipeline: prompt design + testing + threshold calibration
- Common pitfalls: hallucinated scores, context loss, model drift between provider updates
- Production considerations: latency, cost, and human-in-the-loop calibration
Stay tuned — full article coming soon.