Engineering
Practical engineering for AI systems. SDK integration, API design, testing strategies, and production-grade patterns.
5 articlesin Engineering
Structured Outputs from LLMs: JSON Mode, Function Calling, and Schema Enforcement
Practical patterns for getting reliable structured data from LLMs — JSON mode, function calling, schema validation, and fallback strategies.
Prompt Engineering as Software Engineering
Version control, testing, parameterization, and CI pipelines for prompts — treating prompt engineering with the same rigor as application code.
Building Reliable AI Agents with Semantic Kernel
Plugin architecture, memory, planners, and error handling patterns for building production AI agents in .NET with Semantic Kernel.
Testing LLM-Powered Features Without Going Broke
Mock strategies, evaluation harnesses, snapshot testing, and cost-aware CI for LLM-integrated applications.
Integrating Azure OpenAI with ASP.NET Core: A Production Guide
SDK setup, retry policies, streaming responses, and structured outputs for Azure OpenAI in .NET production applications.