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

LLMs are not chatbots: they are architectural components that change products

When a team discovers it can call GPT-4 from an ASP.NET controller in three lines of code, the first reaction is excitement.

The second, a few weeks later, is confusion: the model hallucinates, costs scale unpredictably, users do not understand what is happening, and the system is not testable.

The problem is not the model.

The problem is that an LLM is not a deterministic function: it is a probabilistic component with variable latency, costs proportional to the volume of text processed, and behavior dependent on the context provided.

Integrating it into a real product requires the same architectural decisions you make for any critical component: where is the boundary of responsibility, how do you handle failure, how do you monitor behavior in production.

In this category you find exactly that: not tutorials for calling an API, but reasoning on how to insert LLMs into real systems with Semantic Kernel, RAG, agents, and function calling, while maintaining control over costs, reliability, and output quality.

Semantic Kernel, agents, and pipelines: what to use and when

The .NET AI ecosystem has consolidated around Semantic Kernel as the primary orchestration framework.

It is not the only option, but it has the strongest Microsoft support, the best integration with Azure OpenAI, and the most active community in the .NET ecosystem.

When to use Semantic Kernel: when you need to compose multiple model calls, manage conversational memory, integrate plugins and tools, or build agents that reason over multiple steps. Semantic Kernel is overengineering for a single isolated call.

When to use the OpenAI SDK directly: when you want total control over the payload, have specific streaming or function calling requirements that Semantic Kernel does not expose cleanly, or are building a custom wrapper for your team.

When to build agents: when the problem requires the system to autonomously decide which tools to call, in what order, and based on what reasoning. Agents are powerful but fragile: they require rigorous prompt engineering, explicit fallbacks, and continuous monitoring.

When not to use an LLM: when the problem is deterministic, when latency is critical, when data cannot leave the infrastructure and a local model is not sufficient, or when the cost per query is not sustainable in the business model.

Costs, latency, and reliability: the three constraints that change everything

Anyone who builds a prototype with an LLM never has a cost problem.

Anyone who brings that prototype to production does.

Tokens cost money.

A RAG pipeline with retrieval, reranking, and generation can cost five to fifty times a simple call.

Multiplied by thousands of requests per day, that difference becomes a margin problem.

System design must account for it: shorter prompts, response caching, intelligent document chunking, choosing the right model for the task's complexity.

Latency is the second constraint.

A user interface that waits three seconds for an LLM response without visual feedback loses users.

Streaming the response solves the perception problem, but not the structural one: some pipelines simply cannot be made fast enough for certain use contexts.

Reliability is the third.

Models hallucinate.

Not always, not often, but enough to make an output validation system necessary when the result has an impact on critical decisions or data.

Automated evaluation, human feedback loops, and fallback to deterministic logic are not optional in production.

How to build an AI product that scales beyond the demo

The difference between a demo that impresses and a product that works in production is measured in months of work on aspects that no tutorial shows.

The first is observability: knowing what the user asked, what context was injected into the prompt, what the model responded, and how long it took.

Without this data you cannot improve the system or diagnose failures.

The second is testability: a system that calls an LLM is not testable with traditional unit tests, but it can be designed with replaceable interfaces, model mocks for functional tests, and automated output evaluation on reference datasets.

The third is governance: who can do what with the AI system, what data enters the context, how user consent is managed, what happens when the model produces inappropriate output.

These are not technical questions: they are product and compliance questions that the technical team must be able to raise before they become problems.

In this category the articles address exactly these aspects: not the magic of AI, but the engineering that makes it useful and sustainable.