LLM evaluation
Define expected behaviour, measurable criteria and repeatable evidence for this quality dimension.
AI EVALUATION · PARIMI
AI Evaluation for LLMs, RAG and AI Agents
A practical AI evaluation approach covering correctness, relevance, grounding, safety, tool use, orchestration and regression evidence.
RAG evaluation
Define expected behaviour, measurable criteria and repeatable evidence for this quality dimension.
AI agent evaluation
Define expected behaviour, measurable criteria and repeatable evidence for this quality dimension.
Semantic evaluation
Define expected behaviour, measurable criteria and repeatable evidence for this quality dimension.
LLM-as-a-judge
Define expected behaviour, measurable criteria and repeatable evidence for this quality dimension.
Regression evaluation
Define expected behaviour, measurable criteria and repeatable evidence for this quality dimension.
Safety evaluation
Define expected behaviour, measurable criteria and repeatable evidence for this quality dimension.
Production quality
Define expected behaviour, measurable criteria and repeatable evidence for this quality dimension.
Evaluation should become engineering evidence.
PARIMI connects evaluation datasets, deterministic checks, semantic evaluation, orchestration behaviour and regression so AI quality can be assessed before and after release.