Papers

The choice between a self-hosted LLM and a public API is one of the most critical architectural decisions teams face when building AI products. Both options provide access to language models that can power chatbots, analysis tools, automation systems, and internal assistants. But in production environments, the difference goes far beyond the possibility of generating answers. The choice affects cost structure, latency, data control, security, maintenance complexity, team skil

Building a high-quality chatbot is not just about generating good-looking responses. In production environments, quality is a multidimensional concept that covers a wide range of metrics and requires strict alignment with business expectations. Modern LLM-powered chatbots operate in a dynamic, high-traffic space where even small degradations in output quality can quickly lead to user frustration, increased support load, or financial and reputational risks. Many teams still tr

Building a modern LLM-powered chatbot application is a complex process that is focused not only on making calls to a model via a user interface. Today, production-ready systems require a robust architecture that includes observability, traceability, configuration management, security controls, and continuous health monitoring. Many teams underestimate these foundational components, focusing solely on prompts or model selection - and as a result, face unpredictable issues, sec

Starting a new career in any domain is always challenging and exciting. In the field of data science, it is even more stressful and...

Let's imagine you decided to use the Large Language model in your application. And immediately after taking this decision, you got a lot...