Sign in

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.

Continual Learning with LLMs: Adapting Models to New Information

Continual Learning with LLMs: Adapting Models to New Information

In a dynamic world where knowledge is constantly updated, there is a need for approaches that enable models to learn continuously without complete retraining. Continuous learning aims to overcome these limitations by allowing models to integrate new knowledge while preserving what they already have. One of the key problems in

The complete guide to LLM API integration

The complete guide to LLM API integration

Whether you're building a chatbot, automating workflows, or adding semantic search, API integration is the bridge between your product and AI capabilities. Below, we'll cover everything from REST API basics and model endpoints to SDK integration, embedding API usage, and architecture patterns. Quick Take * API integration

LLM Data Compliance: Privacy and Regulatory Guide

LLM Data Compliance: Privacy and Regulatory Guide

The development of large language models has reached a stage where technological advantage is inextricably linked to the legal purity of processes. The specifics of training and operating such systems involve processing colossal amounts of information, which automatically transforms every model into a potential source of confidential data. While artificial

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.