Toxicity detection classifies text for hate speech, offensive language, harassment, and harmful content. Categories: Hate speech (targeting identity groups), harassment/bullying, threats/violence, sexually explicit, profanity, self-harm content. Approaches: Classifiers: Trained models outputting toxicity scores per category. LLM evaluation: Prompt model to assess content appropriateness. Rule-based: Keyword matching for explicit terms. Models: Perspective API (Google), OpenAI moderation endpoint, HuggingFace toxic-BERT, Detoxify. Challenges: Context dependence (reclaimed language, quotation), evolving language, coded hate speech, cross-cultural variations, false positives on legitimate discussion. Calibration: Set thresholds based on use case - strict for child-facing, looser for research. Multi-lingual: Toxicity patterns differ across languages, need language-specific training. Implementation: Score threshold for blocking, gradual response (warning → block), human review for borderline cases. Integration points: Input filtering, output filtering, content moderation queues. Foundation for content safety systems.