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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.

Preventing LLM Hallucinations: Techniques & Best Practices 2026

Preventing LLM Hallucinations: Techniques & Best Practices 2026

One of the key challenges of modern large language models (LLMs) remains the phenomenon of hallucination - situations in which the model generates convincing but factually false or fabricated information. Despite significant progress in model quality in 2025-2026, this problem has not yet been fully eliminated, especially in open queries,

Transformer architecture explained

Transformer architecture explained

Before the advent of transformers, natural language processing systems struggled to handle long-term dependencies, preserve context, and scale to massively parallel learning. Traditional recurrent neural networks (RNNs) and long short-term memory (LSTM) networks processed language sequentially. The introduction of the transformer model replaced recursion with parallel attention-based

Enterprise LLM Solutions: Custom AI Systems Guide

Enterprise LLM Solutions: Custom AI Systems Guide

A rapid transition toward multimodal systems and autonomous AI agents capable of executing complex sequences of actions in real-time is being observed. This technological wave promises the complete automation of intellectual labor; however, its implementation at the enterprise-scale faces barriers that cannot be overcome with mass consumer solutions.

LLM Comparison 2026: Claude vs GPT vs Gemini vs Open Source Models

LLM Comparison 2026: Claude vs GPT vs Gemini vs Open Source Models

The world of LLMs in 2026 is much more competitive and diverse than it was just a few years ago. Leading players such as OpenAI, Anthropic, and Google are actively improving their models - GPT, Claude, and Gemini, respectively. Each of them has its own approach to security, performance, and user

Optimize LLM costs

Optimize LLM costs

Large Language Models (LLMs) have become the foundational layer of modern AI products. But as implementations scale, so do costs. Many teams are finding out late that LLM pricing can get out of hand, especially as usage grows across multiple functions, users, and environments. But it's possible to

Monitoring LLMs: Track Performance & Detect Issues

Monitoring LLMs: Track Performance & Detect Issues

One of the most specific and dangerous challenges in implementing large language models is hallucinations – situations where the model generates factually false information while presenting it extremely confidently and convincingly. In a business context, this creates direct risks: * providing customers with non-existent discounts or erroneous instructions. * generating content that

Complete Guide to Labeling Workflows for LLM Training

Complete Guide to Labeling Workflows for LLM Training

The modern artificial intelligence industry has traveled the path from simple pattern recognition to the generation of complex meanings. The key difference between LLMs and classic ML lies in the necessity of instruction tuning. Raw texts from the web often contain contradictory information, errors, and biases. Annotation becomes a tool