Sign in

Inna Nomerovska

Inna Nomerovska

Inna Nomerovska is the VP of Marketing and Brand Strategy at Keymakr | Keylabs. She is a tireless technology enthusiast with 15 years of experience in international marketing and startup background.

Complete guide RLHF for LLMs

Complete guide RLHF for LLMs

Reinforcement Learning with Human Feedback (RLHF) is a key approach to aligning large language models (LLMs) with human values and expectations. Rather than relying solely on automated metrics, RLHF uses human ratings to train models to select relevant responses. This method enables LLMs to generate grammatically correct text and exhibit

LLM as a Judge for Automated Output Evaluation

LLM as a Judge for Automated Output Evaluation

The stage of evaluating LLM responses is the most critical stage of development, as it is impossible to improve a product without an objective measurement of quality. However, with the evolution of technology, traditional verification methods have faced serious challenges that make them ineffective in today’s market conditions. Manual

Pedestrian Annotation in Computer Vision

Pedestrian Annotation in Computer Vision

Pedestrians are the most unpredictable and vulnerable category of objects in computer vision systems. Unlike cars, whose trajectory is limited by the roadway and physical characteristics, human movement can change instantly in any direction. That is why high-quality pedestrian labeling has become the foundation for safety in the fields of

RAG LLM Explained: Improving AI Response Accuracy

RAG LLM Explained: Improving AI Response Accuracy

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

Agent Training Data: A Guide to LLM-Based Agent Training

Agent Training Data: A Guide to LLM-Based Agent Training

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,

LLM Agents: Building Autonomous AI Systems That Reason and Act

LLM Agents: Building Autonomous AI Systems That Reason and Act

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

How to Train LLM: A Guide for Enterprise Teams

How to Train LLM: A Guide for Enterprise Teams

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