Generative AI for .NET teams that need real use cases, control, and economic value

This category explains how to bring LLMs, agents, and generative AI into .NET products and processes with technical discipline: less hype, more integration, more reliability, more measurable value.

Analyses, cases, and articles on LLMs, AI agents, and .NET integration patterns

7 articles found

When LLMs become a real leverage point

LLMs become a real leverage point when they are connected to processes, data, and concrete use cases. Without integration they remain an impressive demo; with the right method they become assistants, semantic search engines, intelligent interfaces, and productivity multipliers for technical teams and companies.

Useful technologies for AI and LLM projects

.NET

runtime and libraries for integrating LLMs into enterprise applications

C#

main language for orchestrating AI pipelines with Semantic Kernel

Azure

Microsoft cloud with OpenAI Service, AI Search and Cognitive Services

Sources and references

Attention Is All You Need, Vaswani et al., 2017

The paper that introduced the Transformer architecture.

Attention Is All You Need, Vaswani et al., 2017

OpenAI developer resources

The official OpenAI documentation for GPT APIs, embeddings, and function calling. Essential for understanding the real limits of models, prompt structure, costs, and context management. I cite it because many articles on the subject skip exactly these technical details, which are the difference between a prototype and a system running in production.

OpenAI developer resources

Frequently asked questions

The most common integration is through Semantic Kernel, the Microsoft library that abstracts calls to OpenAI, Azure OpenAI, or local models. Alternatively, you can use the OpenAI SDK for .NET directly. The typical pattern involves a pipeline with memory, plugins, and model call orchestration, not a simple HTTP call.

Semantic Kernel is a Microsoft open source framework for orchestrating AI models in .NET, Python, and Java applications. Use it when you need to compose multiple model calls, manage conversational memory, integrate tools and plugins, or build autonomous agents. For single isolated calls, a direct SDK is simpler.

With .NET you can use GPT-4o and OpenAI models via the official SDK, Azure OpenAI models via Semantic Kernel, open source models like LLaMA or Mistral via Ollama locally, and any API compatible with the OpenAI standard. The choice depends on privacy requirements, latency, cost, and response quality in your specific domain.

Someone who knows AI understands where to place an LLM in the architecture without making it a bottleneck, how to manage token costs, when contextual generation is worth the latency trade-off, and how to fall back to deterministic logic when the model is unreliable. Those who do not tend to use AI as a decorative feature or build fragile dependencies.