Evals (AI Evaluation)
A structured framework for measuring AI agent and model outputs against quantified criteria and detecting regressions
#evals#AI evaluation#agent evaluation#LLM evaluation#benchmark#regression detection
What are Evals?
Evals (Evaluations) are structured frameworks for measuring AI model or agent outputs against defined criteria. Beyond simple pass/fail testing, evals track each step of multi-step tasks and automatically detect quality regressions compared to prior versions.
How do evals differ from regular tests?
Traditional software tests check whether an output matches a fixed expected value. AI evals require different approaches because outputs are not deterministic.
- Criteria-based scoring: Does the output satisfy specific conditions (format, required information, etc.)?
- LLM-as-a-Judge: A more capable model scores the output against a rubric
- Trajectory analysis: Evaluates not just the final answer but the reasoning path taken to reach it
Why do evals matter?
If an agent reaches the right answer via flawed logic, that is a reliability problem waiting to surface. Evals assess both output quality and reasoning soundness.
Related Terms
Related terms
operations
LLM-as-a-Judge
An evaluation methodology where a capable LLM scores another model's or agent's outputs against a predefined rubric
operations
Minimum Viable Agent (MVA)
A smallest-possible agent design that validates one core task first with single-input, single-output execution
operations
Verification Loop
An operational pattern that converges quality by repeatedly testing, reviewing, and retrying AI-generated outputs