The AI Architecture tag covers practical patterns and tooling for building, debugging, and scaling AI workloads in .NET and Azure environments. From comparing Semantic Kernel and LangChain async strategies to diagnosing latency in a multi‑provider orchestration stack, and finally outlining a senior engineer’s playbook for moving prototypes into production, it equips developers with actionable guid…
9 articles since Aug 2026
Explore the real engineering trade‑offs between self‑attention and cross‑attention in .NET Retrieval‑Augmented Generation, with production‑ready patterns, profiling tips, and scaling guidance.
Learn how to engineer advanced BPE tokenizer customization for domain‑specific corpora, integrate it into .NET LLM pipelines, and optimize performance for production workloads.
Explore a production‑ready multi-tenant MCP server design that balances isolation, security, and Azure‑scale for modern SaaS platforms.
Explore the real‑world trade‑offs of Azure OpenAI Service vs GPT‑4 API for .NET microservices, with cost, performance, security, and production‑ready patterns.
Explore the architectural trade‑offs, performance nuances, and .NET integration strategies of Semantic Kernel vs LangChain to make informed engineering decisions.
In thirty-three minutes, MockEvalio went from calling Groq directly from five different services to a centralized orchestrator with versioned prompts and cost-aware multi-provider routing. What built on what, and what's never been touched since.
Discover the concrete architectural, reliability, and cost changes required when moving an AI system from a prototype to a production‑ready .NET service. Learn the hidden pitfalls, scaling patterns, and deployment best practices.