This tag covers practical aspects of integrating large language models into .NET and Azure workloads, from managing prompt size and cost to ensuring observability and deployment. Articles discuss how context length directly impacts billing for .NET developers, how ASP.NET Core can expose trace and metric data for LLM apps, and how to use evals as release gates in CI/CD pipelines. Understanding the…
3 articles since Sep 2026
Explore how attention mechanics inflate the context length cost for .NET developers, see real‑world Azure OpenAI pricing, and get production‑ready strategies to keep token spend under control.
Learn how to instrument, trace, evaluate, and close the feedback loop for large‑language‑model applications built with ASP.NET Core. Real‑world patterns, code, and production tips for Azure OpenAI, Semantic Kernel, and OpenTelemetry.
Learn how to embed evaluator‑driven release gates into your .NET CI/CD flow, ensuring LLM updates meet accuracy, safety, and latency standards before they hit production.