# Write a model card to document a machine learning model

A complete model card covering intended use, data, performance by segment, limitations, fairness checks, and monitoring plan.

- Topic: Data & Analysis
- Framework: RTF
- Level: Standard
- Tags: Write, Analyze, Fill-in Template
- Source: https://www.thinkwithprompt.com/prompts/write-a-model-card-to-document-a-machine-learning-model

## Prompt

```text
Write a model card documenting my machine learning model for reviewers, users, and future maintainers.

**Model name and version:** [NAME]
**Purpose and intended users:** [what it predicts and who uses the output]
**Training data:** [sources, date range, size, label definition, known gaps]
**Model type and key features:** [DETAILS]
**Evaluation results:** [PASTE overall metrics and by segment if available]
**Fairness or bias checks done:** [DETAILS, or "none yet"]
**Deployment:** [batch/real time, where outputs go, refresh cadence]

Use these sections:
1. **Model details:** owner [ADD], version, date, type, dependencies.
2. **Intended use:** primary uses, intended users, and out-of-scope uses it must not be used for.
3. **Training and evaluation data:** sources, time periods, preprocessing, label definition, known representation gaps.
4. **Performance:** metrics with the evaluation method, the baseline compared against, and results by relevant segment.
5. **Fairness considerations:** groups checked, results, and gaps in what was checked.
6. **Limitations and risks:** where it is unreliable, failure modes, and potential harms.
7. **Monitoring and maintenance:** drift checks, performance tracking, retraining triggers, and the rollback plan.
8. **Human oversight:** when people should review or override outputs.

Rules: use only the facts I provide. Where information is missing, insert [ADD] or [CONFIRM] rather than filling it in. Note any applicable regulatory or internal review requirements I should confirm with my governance or legal team.
```

## What to fill in

- NAME
- what it predicts and who uses the output
- sources, date range, size, label definition, known gaps
- DETAILS
- PASTE overall metrics and by segment if available
- DETAILS, or "none yet"
- batch/real time, where outputs go, refresh cadence
- ADD
- CONFIRM

Free to copy and use. From ThinkWithPrompt (https://www.thinkwithprompt.com/), a library of 2,663 curated AI prompts.