Inna Nomerovska

Modern large language models often operate only on the information they absorbed during their training. This creates serious limitations, as any model's knowledge is "frozen" at the moment its training concludes. If an event happened yesterday, it simply does not exist for the neural network, making

Agents based on Big Language Models (LLMs) are now changing the way businesses, researchers, and developers interact with data and make decisions. The success of these agents depends on the model architecture, quality, diversity, and management of the training data. This guide provides an overview of the sources, generation methods,

The rapid development of LLMs has significantly influenced modern approaches to building intelligent systems. Models capable of processing natural language, code, and multimodal data are increasingly used in applied tasks related to information analysis, decision support, and automation of complex processes. The relevance of agent-based approaches based on large language

Enterprise teams are increasingly turning to large-scale language models (training large language models) to automate workflows, improve decision-making, and engage with customers. Successful LLM model training requires not only access to a pre-trained model but also a structured, step-by-step approach that aligns the model with your organization's specific

Modern warehouses process hundreds of thousands of items daily, which are constantly in motion, and the demand for speed is only increasing. Although many of them use intelligent systems, such as scanners, robots, and management software, these tools do not see the full context. They know that a product should

Modern supply chains are the foundation of global trade, but they have become incredibly complex. Companies process vast volumes of data daily regarding demand, inventory, transport, and logistics. The main problem is that many key processes are still performed manually. People use outdated spreadsheets and rely on their own intuition