AI Evals
Reproducible test suites that measure LLM output quality across model, prompt and code changes.
/ quick answer
Evals are the unit tests of AI systems. You define a labeled dataset and scoring functions (exact match, rubric grading, LLM-as-judge) and run them on every change — so you catch regressions when swapping models, tweaking prompts or upgrading a tool.
/ frequently asked
What is AI Evals?
Evals are the unit tests of AI systems. You define a labeled dataset and scoring functions (exact match, rubric grading, LLM-as-judge) and run them on every change — so you catch regressions when swapping models, tweaking prompts or upgrading a tool.
What is an example of AI Evals?
Before promoting a new system prompt, a team runs 200 saved customer questions through both the old and new prompt and compares helpfulness, accuracy and refusal rates side by side.
Why does AI Evals matter?
Reproducible test suites that measure LLM output quality across model, prompt and code changes.
What should I learn after AI Evals?
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.
- →EU AI Act Compliance
EU AI Act Compliance refers to adhering to the regulatory framework established by the European Union to govern the development, deployment, and use of artificial intelligence systems within the EU.
- →AI Governance Framework
An AI Governance Framework is a structured system of policies, processes, roles, and standards designed to guide the responsible, ethical, and compliant development and deployment of artificial intelligence systems within an organization.
- →Guardrails
Runtime checks that constrain LLM inputs and outputs to keep behavior safe and on-spec.
- →Prompt Versioning
Treating prompts as code: tracked, diffed, rollback-able.
Related workflows
Turn this into a repeatable process.
- →AI Agent Monitoring System
Track agent runs, failures, cost, and review queues from one operational surface.
- →Prompt Library Operations
Version, evaluate, and reuse prompts as operational assets rather than loose text snippets.
- →Data Residency Audit Workflow
This workflow details the systematic steps for auditing an organization's data storage and processing locations to verify compliance with various data residency regulations.
- →AI Risk Assessment Workflow
This workflow systematically identifies, analyzes, and evaluates potential risks associated with the development and deployment of Artificial Intelligence systems, guiding mitigation strategies.
Related tool stacks
The tools that run it in production.
- →AI Ops Observability Stack
Monitoring layer for agent runs, workflow health, cost, errors, and review queues.
- →AI Compliance Monitoring Stack
This stack provides a set of tools and technologies for continuously monitoring AI systems to ensure ongoing adherence to regulatory requirements like the EU AI Act and data privacy laws.
- →Agent Economics Observability Stack
This stack provides tools to monitor, analyze, and optimize the economic performance of AI agents, focusing on token costs, performance, and ROI.
- →AI Testing Stack
Test deterministic code and probabilistic AI output in one pipeline.
Related prompts
Reusable prompts for this job.
- →No-Code Automation Spec Writer
Turn a vague 'I want to automate X' into a buildable scenario spec for Make / n8n / Zapier.
Comparisons & alternatives
Pick between the options.
- →ChatGPT vs Claude
Two leading conversational AI assistants compared across reasoning, writing, coding, and pricing.
- →Lovable vs Bolt
Two AI app builders compared on speed, backend, deployment, and production readiness.
- →OpenAI API vs Anthropic API
Choosing between the two leading LLM API providers for production apps.
- →Notion vs Airtable for AI Ops
Which one should run your AI workflow review queues and content calendar?