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7 API Debugging Problems Developers Face Every Day — and How to Solve Them Faster

You send a request to an API. The endpoint looks correct, the server is running, and the payload appears valid. Yet the response is 400 Bad Request , the authentication fails, or the application returns an error that tells you almost nothing useful. Sound familiar? API debugging is a regular part of backend and full-stack development. Even experienced developers can lose time investigating a problem that eventually turns out to be a missing JSON property, an incorrect content type, an expired token, or a mismatch between a request payload and a C# model. The challenge is not simply finding the error. It is identifying which layer of the request is failing and why . In this guide, we'll walk through seven common API debugging problems, explain how to investigate them systematically, and show practical techniques you can use in .NET and other web applications. We'll also look at a few lightweight developer tools that can simplify everyday tasks such as formatt...
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LLM Metrics: A Practical Guide to Measuring AI Application Quality

LLM Evaluation in .NET: Metrics, RAG, Agents and AI Testing Building an application with a large language model is relatively easy. Making sure that application continues to produce useful, accurate, reliable, safe, and affordable results is much harder. An LLM can generate a response in a few seconds, but the fact that it generated an answer does not tell us whether the answer was good. Was it correct? Did it actually answer the user's question? If the application uses RAG, did the answer come from the retrieved information? If an AI agent was involved, did it select the correct tool? Did the tool receive valid arguments? There are also operational questions: How many tokens were consumed? How much did the request cost? How long did the user wait? Did the system use unnecessary tools or model calls? Did anything get worse after a prompt, model, or retrieval change? These questions make LLM evaluation an important part of production AI engineering. Tradition...
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Vector Databases in .NET: Embeddings, Semantic Search, RAG & pgvector

Vector Databases in .NET: RAG, pgvector, HNSW and Production Design If you are building AI features in a .NET application, calling an LLM is often the easy part. The harder problem starts when the model needs information that is specific to your application. That information might be stored in an internal policy document, product catalog, support ticket, technical manual, compliance document, source repository, or company knowledge base. An LLM does not automatically know how to search those systems simply because your application can access them. Your application needs a retrieval layer that can identify useful information and provide it to the model. This is where embeddings, vector search, and vector databases become useful. A vector database stores numerical representations of information called embeddings and provides mechanisms for retrieving records according to vector similarity. This makes vector search an important building block for RAG applications, semantic ...
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