LLM and agent evaluation

LLM Evaluation for AI Agents

Evaluate generated answers with semantic criteria while keeping deterministic facts, execution integrity and security under explicit controls.

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LLM evaluation is one layer of AI assurance. It is useful when a result requires interpretation, but it should not be treated as a universal truth signal or as a substitute for deterministic system validation.

Semantic quality

Evaluate relevance, correctness, usefulness, completeness and other criteria that require interpretation of language and context.

Answer relevance
Helpfulness and completeness
Conversation quality

LLM-as-a-judge controls

Preserve the evaluation criterion, score, threshold, reason and input evidence so semantic judgments remain inspectable.

Explicit criteria
Thresholds
Judge configuration

Separate retrieval from generation

For RAG systems, evaluate retrieval quality separately from generator quality so a strong answer cannot hide poor retrieval or unsupported context.

Context relevance
Recall and precision
Faithfulness and answer relevance

Connect scores to execution

Semantic evaluation becomes more useful when connected to the actual conversation, trace, deterministic assertions and release gate.

Evidence chain
Execution integrity
Release disposition

Related Shyena capabilities

Vera Evaluation model AI agent testing RAG assurance

Frequently asked questions

What is LLM evaluation?

LLM evaluation measures generated outputs against defined quality criteria such as relevance, correctness, completeness, faithfulness or usefulness.

What is LLM-as-a-judge?

LLM-as-a-judge uses another model to assess an output against explicit criteria. It is a semantic evaluation mechanism, not a replacement for deterministic assertions or security controls.

Can LLM evaluation be used in release gates?

Yes, provided the criteria, thresholds, evidence and failure semantics are explicit and combined with the other assurance layers required by the system.

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