Blog
Mert Özen's blog posts on software development, backend, frontend, and DevOps.
The LLM Context Window: What the Model Remembers and What It Silently Cuts
LLM APIs are stateless; you send the chat history with every request and every turn grows the bill. Even though Gemma 3 supports 128K tokens, Ollama allocates a 4096-token window by default and cuts a 4498-token prompt down to 2051 tokens without any error. We set num_ctx in C# and TypeScript and try managing history with trimming and summarizing, using real output.
Tokens and Tokenizers in LLMs: Why Does Turkish Take More Tokens?
What is a token, and how do BPE and SentencePiece split text? Using Ollama's prompt_eval_count counter we measure the same sentence in Turkish and English with the gemma3:4b and qwen3:4b tokenizers, then see with real numbers how many tokens numbers, GUIDs, JSON and code take, the hidden tokens of the chat template and how tokens affect cost, limits and speed.
Your First LLM Request: The First Answer with Ollama, C# and TypeScript
We get to know Ollama, look at models with ollama list and ollama ps, and send the first request to the /api/chat endpoint with curl. Then we repeat it in C# with Microsoft.Extensions.AI and OllamaSharp and in TypeScript with the ollama package, and use real output to read the token counts, the nanosecond durations and why the first request is slow.
What Is an LLM? Large Language Models from a Developer's Point of View
A large language model is a function that predicts the next token. We build that loop token by token with a local gemma3:4b, look at training versus inference, base versus instruct, open-weight versus closed and local versus cloud, and use real outputs to see where the model goes wrong on dates, arithmetic and order questions.