What is Evaluation?

Explained

Evaluation assesses how well an AI model performs—on accuracy, relevance, safety, bias, and other metrics. Robust evaluation is essential before deploying models to production.

Decision guides that use this term

• Single AI vendor vs multi-model strategy: should you bet on one provider?
• AI pilot vs production: what actually changes when you scale?
• Agent framework vs custom orchestration: should you adopt a framework or write your own agent loop?
• LLM evals vs human review: how should you assure the quality of enterprise AI?
• Reasoning models vs standard LLMs: when is the extra thinking worth paying for?
• Synthetic data vs real data: what should you train, tune and test your AI on?

Related terms

• Bias Distortion in data or model that produces unfair or inaccurate results.
• Production Environment where the model serves real users (vs. development/testing).
• API Interface that allows different systems to communicate; key for integrating AI models.
• API Specification Formal description of endpoints, schemas, and errors—e.g., OpenAPI—used for docs, mocks, and validation.
• Automation Executing tasks without human intervention, often with AI for complex decisions.
• Autonomous Systems Systems that operate without continuous supervision, making decisions in real time.