# What Are AI Evals? AI Evaluation Explained | Gr8r | Gr8r Studio

> AI evaluation, or evals, is structured testing of an AI system on real examples to measure quality before and after release. How evals work and what to avoid.

Source: https://gr8rstudio.com/glossary/ai-evaluation

Glossary · AI

# AI evaluation (Evals)

AI evaluation, often called evals, is the structured testing of an AI system against a set of realistic examples with agreed success criteria, so teams can measure quality, catch regressions and decide whether it is ready to release.

[Ship AI you can measure](https://gr8rstudio.com/services/product-engineering)[All terms](https://gr8rstudio.com/glossary)

In one line

AI evaluation, often called evals, is the structured testing of an AI system against a set of realistic examples with agreed success criteria, so teams can measure quality, catch regressions and decide whether it is ready to release.

Related

- [AI hallucination](https://gr8rstudio.com/glossary/ai-hallucination)
- [Prompt engineering](https://gr8rstudio.com/glossary/prompt-engineering)
- [Human-in-the-loop](https://gr8rstudio.com/glossary/human-in-the-loop)

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On this page

1. [Why AI evaluation matters](#s-0)
2. [How AI evaluation works](#s-1)
3. [Common AI evaluation mistakes](#s-2)
4. [How Gr8r approaches AI evaluation](#s-3)

## Why AI evaluation matters

AI output changes when prompts, models or data change. Without evals, teams judge quality by gut feel and a few demos. Evals turn "it seems fine" into evidence that can support a release decision.

## How AI evaluation works

Teams collect real inputs, including awkward edge cases, and define what a good output looks like for each. The system runs against the set, and results are scored by rules, by people, or by another model checked by people.

The same set is rerun whenever something changes.

## Common AI evaluation mistakes

Testing only easy, clean examples. Using one overall score that hides serious failures. Letting the team that built the feature be the only judge of it.

## How Gr8r approaches AI evaluation

We agree success criteria with you before building. We build the eval set from your real data, include failure cases on purpose, and share the results, including the limits, before any launch.

Example

Before launching an invoice extraction feature, a team tests it on 200 real invoices, including blurry scans and foreign currencies, and tracks accuracy per field rather than one overall number.

FAQ

## Questions

Ask anything else

Reading our answers…

- How many examples does an eval set need?
Enough to cover the main task types and known edge cases. Start small with real examples and grow the set as you find new failures.
- Can an AI grade another AI?
It can help at scale, but its judgements should be checked against human ratings on a sample, because the grading model can be wrong too.

## Related terms.

- [ 01 AI hallucinationAn AI hallucination is output from a language model that sounds confident but is false, made up or not supported by its sources, such as an invented citation, figure or policy. Explore ](https://gr8rstudio.com/glossary/ai-hallucination)
- [ 02 Prompt engineeringPrompt engineering is the practice of writing, structuring and testing the instructions, context and examples given to a language model so it produces useful, consistent output for a specific task. Explore ](https://gr8rstudio.com/glossary/prompt-engineering)
- [ 03 Human-in-the-loopHuman-in-the-loop (HITL) is a design approach where people review, approve or correct an AI system's output at defined points, so important decisions are checked by someone accountable before they take effect. Explore ](https://gr8rstudio.com/glossary/human-in-the-loop)

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