Category:
Category:
Evaluation (LLM/Agent Evaluation)
Category:
Evaluation & Quality
Definition
Assessing correctness, safety, robustness, and task success of LLMs and agent systems.
Explanation
Evaluation ensures AI systems behave reliably. LLM evaluation measures accuracy, truthfulness, reasoning quality, safety, compliance, and generalization. Agent evaluation additionally tests tool call correctness, sequence validity, autonomy level, task success rate, trace accuracy, and robustness to noisy inputs. Enterprises require strong evaluation loops for governance, vendor comparisons, and deployment decisions.
Technical Architecture
LLM/Agent → Evaluation Suite → Metrics Engine → Report → Model Ranking or Retraining
Core Component
Benchmark datasets, safety tests, metrics engines, human-in-the-loop validators, red-team harness
Use Cases
Model selection, vendor comparison, production audits, governance automation
Pitfalls
Benchmark overfitting, synthetic bias, missing domain-specific tests
LLM Keywords
LLM Evaluation, Agent Testing, Safety Evaluation
Related Concepts
Related Frameworks
• Guardrails
• Evaluation Benchmarks
• Safety
• Agent Traces
• Evaluation Pipeline
• AI Quality Matrix