AI Evaluation
AI evaluation is the measurement layer of an AI system: a fixed set of cases, a scoring method and a tracked pass rate you can regress against.
/ quick answer
An eval has three parts: a dataset of inputs with expected outcomes, a scorer (exact match, assertion, or model-graded rubric), and a recorded score over time. Without evals, every prompt or model change is a guess. The dataset should grow from real production failures, not invented examples.
What is AI Evaluation?
An eval has three parts: a dataset of inputs with expected outcomes, a scorer (exact match, assertion, or model-graded rubric), and a recorded score over time. Without evals, every prompt or model change is a guess. The dataset should grow from real production failures, not invented examples.
What is an example of AI Evaluation?
40 stored support tickets with expected tags and required facts; any prompt change must keep the pass rate above 92%.
Why does AI Evaluation matter for AI and automation?
AI evaluation is the measurement layer of an AI system: a fixed set of cases, a scoring method and a tracked pass rate you can regress against. It connects to the workflows, prompts and tool stacks linked on this page, so you can move from definition to execution without leaving Onexial.
/ continue exploring
Related concepts
The vocabulary this page depends on.
- →Evals
Automated tests that grade LLM outputs against expected behavior.
- →LLM-as-Judge
Using a strong model to grade another model's output.
- →Agent Cost Control
Agent cost control is the practice of budgeting tokens, steps and model tiers per task so autonomous systems stay economically viable at scale.
- →AI Testing
AI testing covers two things: using AI to generate and maintain tests, and testing AI systems whose output is non-deterministic.
Related workflows
Turn this into a repeatable process.
- →Build an Eval Suite Before Optimising Prompts
Stop guessing whether a change improved anything.
- →Monitor an AI System in Production
See quality, cost and failure drift before your users report it.
- →Build a Test Suite for a Non-Deterministic AI Feature
Grade probabilistic output without brittle snapshot tests.
- →Cut Agent Costs by 60% Without Losing Quality
A measurable cost-reduction pass for any agent already in production.
Related tool stacks
The tools that run it in production.
- →AI Observability Stack
Traces, cost, evals and quality drift for AI systems in production.
- →AI Testing Stack
Test deterministic code and probabilistic AI output in one pipeline.
- →AI Security Stack
Least-privilege tooling, approval gates and audit trails for agentic systems.
Related prompts
Reusable prompts for this job.
- →Eval Rubric Prompt
Builds a scoring rubric a grader model can apply consistently.
- →AI Code Review Rubric Prompt
A reviewer prompt that finds real defects instead of style opinions.
- →AI System Threat Model Prompt
Produces a concrete threat model for an AI system with tool access.
Related use cases
How people apply it, and what came out.
- →Platform Catches a 19% Quality Drop Before Users Did
Continuous sampling and evals caught silent degradation after a model update.
Comparisons & alternatives
Pick between the options.
- →Model-Graded Evals vs Assertion Evals
Assertions are cheap, fast and objective; model grading captures quality you cannot express as a rule.