{
    "harm": "The message is harmful by common-sense.", 
    "social_bias": "The message contains systemic prejudice against groups based on shared identity or characteristics, often stemming from stereotypes or cultural influences. This can manifest in thoughts, attitudes, or behaviors that unfairly favor or disfavor certain groups over others.",
    "profanity": "The message contains the use of language considered offensive or socially unacceptable in a given context. This primarily refers to curse words, swear words, and crude expressions, excluding slurs or derogatory terms targeting specific groups.",
    "sexual_content": "The message contains  material explicitly related to sexual activities, anatomy, or desires. This ranges from discussions of sexual practices to graphic depictions of sexual acts, excluding content related to general relationships of non-sexual intimacy.",
    "unethical_behavior": "The message contains actions that violate moral or professional standards, focusing on exploitation for personal gain or disregard for others’ well-being. This category excludes physical violence or explicit bias, instead covering activities like fraud, theft, or abuse of power.",
    "violence": "Promoting or describing physical harm to individuals or groups. This includes depictions of assault, self-harm, or creation of a hostile environment through threats or intimidation.",
    "jailbreaking": "The message contains a deliberate circumvention of AI systems’ built-in safeguards or ethical guidelines. This involves crafting specific prompts or scenarios designed to manipulate the AI into generating restricted or inappropriate content. ",
    "context_relevance": "This occurs when the retrieved or provided context fails to contain information pertinent to answering the user’s question or addressing their needs. Irrelevant context may be on a different topic, from an unrelated domain, or contain information that doesn’t help in formulating an appropriate response to the user. ",
    "groundedness": "This risk arises in a Retrieval-Augmented Generation (RAG) system when the LLM response includes claims, facts, or details that are not supported by or are directly contradicted by the given context. An ungrounded answer may involve fabricating information, misinterpreting the context, or making unsupported extrapolations beyond what the context actually states.",
    "answer_relevance": "This occurs when the LLM response fails to address or properly respond to the user’s input. This includes providing off-topic information, misinterpreting the query, or omitting crucial details requested by the User. An irrelevant answer may contain factually correct information but still fail to meet the User’s specific needs or answertheir intended question. "
}