What this capability evaluates
Apply repeatable quality criteria to the behaviors and signals that matter for this part of the AI workflow.
Run repeatable evaluations on LLM outputs using deterministic checks, semantic methods, model-based judges, custom evaluators, and human review.
Part of the Nexoqo AI QualityOps platform
Run repeatable evaluations on LLM outputs using deterministic checks, semantic methods, model-based judges, custom evaluators, and human review.
Apply repeatable quality criteria to the behaviors and signals that matter for this part of the AI workflow.
Measure correctness, relevance, completeness, groundedness, consistency, safety, and business-specific criteria.
Use criteria that reflect the complete AI workflow and the quality requirements of the product.
Include this signal in a repeatable evaluation and compare it across AI-system changes.
Include this signal in a repeatable evaluation and compare it across AI-system changes.
Include this signal in a repeatable evaluation and compare it across AI-system changes.
Include this signal in a repeatable evaluation and compare it across AI-system changes.
Include this signal in a repeatable evaluation and compare it across AI-system changes.
Include this signal in a repeatable evaluation and compare it across AI-system changes.
Include this signal in a repeatable evaluation and compare it across AI-system changes.
Include this signal in a repeatable evaluation and compare it across AI-system changes.
Measure correctness, relevance, completeness, groundedness, consistency, safety, and business-specific criteria.
Connect test results with regression analysis, production monitoring, and defined quality thresholds as the AI system evolves.
See how it worksConnect this workflow with the other layers of continuous AI evaluation.
Discuss how continuous evaluation can fit the AI systems and quality criteria your team is building.