Source: [Original HTML page](https://stolenorbit.com/en/templates/ai-evaluation-dataset/)

Language: English

FREE TEMPLATE · FILL IN, DOWNLOAD, REUSE

# AI evaluation dataset

Build a collection of cases that makes versions comparable and exposes failures before they reach live operations. The quality of your reference answers matters as much as the model.

[By Stolen Orbit](https://stolenorbit.com/en/about/) Revision 2026-09-24

## When to use it and what to produce

[Source for this section](https://stolenorbit.com/en/templates/ai-evaluation-dataset/#template-purpose)

Before selecting an AI configuration, changing instructions or sources, or extending a project to new documents and teams.

A case register with verified expected results, judgement criteria and a separate set for the final evaluation.

Fill in the fields below and download your document. The website does not send or save answers: closing the page may lose them. Examples are illustrative and should be replaced with your process data.

[Download the complete blank template (.md)](https://stolenorbit.com/downloads/evaluation-dataset-en.md)

Without JavaScript, download the full template above and complete it in your editor.

## 1\. Cover real work

[Source for this section](https://stolenorbit.com/en/templates/ai-evaluation-dataset/#coverage)

Define the task before collecting examples. Include cases where asking for help or declining to answer is the correct behaviour.

**See a worked example**

Illustrative example: a deadline extraction task includes documents with no date and conflicting dates. For an ambiguous document, “review required” can be the correct result.

**Task, input and required output**

Specify what the system receives, what it should produce and actions it must not perform.

**Categories to represent**

List frequency, languages, formats, ambiguous cases, missing information and high-consequence situations.

**Case sources and selection**

Record period, permission to use the data, planned de-identification and limits of sample representativeness.

## 2\. Create verifiable references

[Source for this section](https://stolenorbit.com/en/templates/ai-evaluation-dataset/#reference)

Give each case a stable identifier. Preserve the reason for its expected answer so disagreements can be resolved without relying on memory.

**See a worked example**

Illustrative example: DOC-017 contains two dates, neither labelled as the deadline. The reference requires no invented deadline and a pointer to the lines needing review.

**Case identifier and input**

Link the approved file or record and its version; avoid sensitive information in case names.

**Expected output and evidence**

Describe correct fields, supporting source, acceptable tolerances and behaviour when an answer is unavailable.

**Reviewer and disagreements**

Record who validates the reference and who resolves differences between evaluators.

## 3\. Compare versions

[Source for this section](https://stolenorbit.com/en/templates/ai-evaluation-dataset/#comparison)

Keep some cases apart from routine improvement work. Report results by category instead of hiding critical failures inside an overall average.

**See a worked example**

Illustrative example: a new configuration extracts more dates but invents a deadline in the ambiguous case. That critical defect remains visible even if the average improves.

**Development and final evaluation**

Record which cases may guide improvements and which remain separate for evaluation.

**Measures and acceptance conditions**

Define critical failures, field correctness, review requests and agreed limits on residual human effort.

**Version, results and decision**

Save configuration, date, category-level results, unresolved defects and the reason to release or defer.

## Pitfalls to avoid

[Source for this section](https://stolenorbit.com/en/templates/ai-evaluation-dataset/#template-pitfalls)

-   Using an unchecked AI answer as the reference result.
-   Improving against the same small set and calling it an independent evaluation.
-   Counting a well-formatted but unsupported answer as a success.

[Turn documents into usable data, with checks at every handoff.](https://stolenorbit.com/en/services/ai-document-data-extraction/)

[Find answers in your company’s approved knowledge.](https://stolenorbit.com/en/services/internal-ai-knowledge-assistant/)

[RAG or fine tuning: first identify the error you need to fix.](https://stolenorbit.com/en/resources/rag-or-fine-tuning/)

[Evaluate an AI system by what must succeed and what must never happen.](https://stolenorbit.com/en/resources/evaluating-ai-reliability/)

[All templates and tools](https://stolenorbit.com/en/tools/)

[All guides](https://stolenorbit.com/en/resources/)

## Take the next step together.

Use this document to start a discussion about scope, evaluation and project ownership.

Review the text before sending. Only your email is needed alongside the message; other details are optional.

[Contact us](/en/contact/)

## Turn the question into a next step.

-   [Evaluating AI reliability](https://stolenorbit.com/en/resources/evaluating-ai-reliability/)
-   [Evaluation lab](https://stolenorbit.com/en/tools/ai-extraction-evaluation/)
-   [Human review for AI](https://stolenorbit.com/en/resources/human-review-for-ai/)