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AI models are powerful but don't naturally understand consequences. Without proper training, they can generate harmful, biased, or misleading content.
You translate high-level safety concerns into specific examples the model can learn from.
Filter risky content – Flag harassment, unsafe material, or content that violates policies
Tag sensitive topics – Mark health, finance, or legal content for stricter handling
Check for bias – Remove responses that stereotype or discriminate against groups
Label safe answers – Judge which responses are acceptable for sensitive prompts
Create safety patterns – Refine how models handle requests for harmful information
Apply safety rubrics – Follow guidelines that define acceptable vs. blocked outputs
Adversarial testing where you intentionally try to break the model's safety rules.
Write challenging prompts – Design tricky requests to test safety boundaries
Document failures – Flag any harmful outputs the model produces
Turn failures into training – Use what breaks to make the model safer
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