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.
/ frequently asked
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?
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.
What should I learn after AI Evaluation?
Open the related workflows and prompts further down this page: they show how this idea is applied in a real task, step by step.
/ 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.
- →LLM Cost Optimization Stack
The layers that keep AI spend predictable as usage grows.
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.