How to Achieve Predictable, Measurable Results with Modern LLMs
In this session we demonstrate how configurable AI evaluation enables operational control in dynamic service environments.
Topics we cover:
- Configuring AI-based evaluation criteria in natural language
- Calibrating strictness and management priorities in real time
- Detecting subtle behavioural and reputational risk signals
- Translating quality insights into steering decisions
What participants get:
- A clear framework for managing AI in Quality Assurance
- Practical examples of configurable AI metrics (zero-shot/few-shot)
- Understanding of how AI evaluates meaning and context
- A demonstration of live metric adjustment and its impact
- Greater operational control over service quality fast
- Faster adaptation to strategic or regulatory changes ai
- Reduced dependency on rigid scorecards and manual QA
- Quality metrics that directly support workforce and coaching decisionss
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