AI system card generator
When you ship an AI system, a system card is how you tell users, regulators, and your own team what it is for, where it should not be used, what could go wrong, and how you tested it. This generator produces a system card in the structured style pioneered by Meta and OpenAI, turning your scattered notes on intended uses, risks, mitigations, and red-teaming into a clean Markdown document you can publish or attach to a deployment.
System card vs model card — the key distinction
A model card documents a single model: training data, performance benchmarks, known biases, and limitations of the weights themselves. A system card documents the full deployed product: the model plus its instructions, guardrails, retrieval system, human-review layer, intended-use policy, and what happened when adversarial testers tried to break it.
The distinction matters because the same base model can behave very differently depending on the system built around it. Regulators, enterprise buyers, and the EU AI Act are increasingly interested in the system, not just the model. This generator targets the system layer.
How it works
You fill in the system’s identity (name, purpose, underlying model, owner), then its intended uses and explicit out-of-scope uses, its known risks each paired with a mitigation, and the evaluations and red-teaming you carried out. The generator assembles these into the canonical system-card sections — overview, intended and out-of-scope uses, risks and mitigations, evaluations, red-teaming findings, and limitations — and renders the whole thing as copy-ready Markdown. Empty sections are flagged with placeholders so reviewers can see what is still missing rather than assuming it was considered.
The anatomy of a strong system card
The sections that matter most, in practice:
Intended uses — describe the specific tasks the system is designed for, the expected user population, and the deployment environment. Vagueness here leads to misuse; precision helps both users and your defence if something goes wrong.
Out-of-scope uses — this is where most teams underinvest. List what the system must not be used for, as specifically as you can. For example, “not for medical diagnosis” is stronger than “not for high-stakes decisions.” This is your primary safety and liability instrument.
Risks and mitigations — for every identified risk, show the control. A risk list without corresponding mitigations reads as a known-but-unaddressed problem.
Red-teaming findings — the most credible section of any card. Document the attacks you tried, including the ones that succeeded and how you fixed them. Claiming no successful attacks were found is the least credible thing you can write.
Evaluations — performance on your chosen benchmarks, including any bias evaluations and how they were run. If you used external evaluators, say so.
Tips and notes
- Out-of-scope uses are the most valuable section. Stating clearly what the system should not do is your strongest safety and liability tool — be specific.
- Pair every risk with a mitigation. A risk list without controls reads as a confession; the system-card format expects you to show what you did about each.
- Report red-teaming honestly. Documenting the attacks that succeeded (and how you fixed them) builds far more trust than claiming none were found.
- Version and date the card. Treat it as living documentation that updates with each material model or guardrail change.
- Publish it. A system card only builds trust when people can read it. Even an internal audience deserves this clarity.