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Tatiana Verbitskaya

Tatiana Verbitskaya

Tatiana Verbitskaya is a Solutions Engineer dedicated to solving complex problems. In addition to her client-facing work at Keymakr, she is also an active contributor to the computer vision community.

LLM Implementation Case Studies: AI Deployment Success Stories

LLM Implementation Case Studies: AI Deployment Success Stories

In recent years, LLMs have become one of the key drivers of the transformation of business, science, and everyday life. From automating customer support to complex data analysis and content generation, language models have demonstrated the ability to significantly increase process efficiency and open new opportunities for innovation. However, the

LLM security and consistency

LLM security and consistency

As large language models (LLMs) have become more advanced, their capabilities have expanded. Today, these systems are used in customer support, legal analysis, healthcare, and enterprise automation. However, a critical responsibility arises: ensuring that AI systems are safe and aligned with human values. The safety and consistency of AI models

LLM Bias Mitigation: Detecting & Reducing AI Model Biases

LLM Bias Mitigation: Detecting & Reducing AI Model Biases

AI and LLMs are widely used in everything from chatbots to automated text processing. However, along with their benefits, they can also reproduce biases embedded in the training data, leading to inaccurate, unfair, or even discriminatory results. The issue of bias in LLMs is important because these models influence the

LLM training data quality

LLM training data quality

Data quality is essential for training large language models (LLMs). High-quality data annotation, careful dataset curation, and effective data filtering ensure the accuracy and reliability of models. Adherence to annotation standards and implementation of quality assurance systems can minimize errors, reduce bias, and improve performance in real-world applications. Quick take

Few-Shot Learning with LLMs: In-Context Learning Explained

Few-Shot Learning with LLMs: In-Context Learning Explained

The key mechanism that enables models to generate text, translate, create summaries, and solve complex tasks is Few-Shot Learning — an approach in which a model learns a new task after being shown only a few examples of how to solve it. In-Context Learning, where the model learns directly while interacting

LLM output optimization

LLM output optimization

Scaling large language systems can increase maintenance costs if memory and maintenance design are ignored. Stacking transformer layers improves accuracy but also increases memory usage and the cost per call. Decoder-only models process tokens in two stages during model inference: a pre-fill stage that relies on GPU computation and a

Complete Guide to LLMs for Synthetic Data

Complete Guide to LLMs for Synthetic Data

The modern development of many artificial intelligence models requires volumes of information that exceed all available supplies of high-quality content on the internet. Synthetic generation is becoming the only way to provide algorithms with the fuel they need for training. Since synthetic data does not belong to real individuals, its