| Troubleshooting | L37-L41 | Diagnosing and resolving Azure AI Content Safety API errors, including HTTP status codes, common failure causes, and recommended fixes or retries. |
| Best Practices | L42-L46 | Tuning Content Safety thresholds, categories, and prompts to reduce misclassifications, plus strategies to balance safety, recall, and user experience. |
| Decision Making | L47-L52 | Guidance on migrating apps from Content Safety preview to GA and deciding when and how to use limited-access Content Safety features and models. |
| Architecture & Design Patterns | L53-L57 | Architectural guidance for combining cloud, hybrid, and on-device Azure AI Content Safety, including design patterns, deployment options, and integration strategies. |
| Limits & Quotas | L58-L64 | Language coverage, building and training custom safety categories, and detecting protected/third‑party code in model outputs. |
| Security | L65-L69 | Details on how Azure AI Content Safety encrypts data at rest, including encryption models, key management options, and compliance/security considerations. |
| Configuration | L70-L75 | Configuring Content Safety runtime via Docker containers and setting up/managing text blocklists to customize and enforce content filtering rules |
| Integrations & Coding Patterns | L76-L80 | Using the groundedness detection API to check if AI responses are supported by source content, with request/response formats, parameters, and integration patterns |
| Deployment | L81-L86 | How to install, configure, and run Azure AI Content Safety Docker containers for text, image, and prompt shield analysis in your own environment. |