1 min read
Securing AI Applications: Authentication and Authorization Patterns
AI applications face unique security concerns: protecting training data, controlling model access, securing inference endpoints, and preventing prompt…
AI Applications
9 articles
1 min read
Testing AI Applications: Strategies for LLM-Based Systems
Test AI components within the full application context, including error handling, timeout behavior, and integration with downstream systems.
1 min read
Implementing Zero-Trust Security for AI Applications
Zero-Trust security ensures AI applications remain protected while enabling legitimate business use cases.
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Monitoring AI Applications with Azure Monitor and Application Insights
Configure alerts for latency spikes, error rate increases, and token usage anomalies to catch issues before they impact users.
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Azure Functions with AI: Building Serverless AI Applications
Configure appropriate timeout settings for AI operations and implement retry logic for transient failures. Consider using premium plans for…
1 min read
Implementing RAG with LangChain and Azure OpenAI
Experiment with chunk sizes, overlap, and retrieval strategies. Hybrid search combining semantic and keyword matching often provides the best results for…
1 min read
Azure Cosmos DB for AI Applications: Vector Search and Change Feed Patterns
Use Change Feed to trigger embedding generation and synchronization with other AI services automatically when documents are created or updated.
1 min read
Prompt Engineering Patterns for Enterprise Applications
Include examples in prompts to guide model behavior for domain-specific tasks. This is especially effective for classification and formatting tasks where…
1 min read
Semantic Kernel v2: Building Composable AI Applications in C#
Semantic Kernel v2 brings enterprise-grade patterns to AI development, making it easier to build maintainable, testable AI applications.