LLM Safety refers to the practices and methodologies designed to ensure that Large Language Models (LLMs) operate responsibly, ethically, and without causing harm. It involves aligning LLMs with societal values, reducing biases, and mitigating risks like harmful content, misinformation, or...

LLM Observability refers to the practice of gaining in-depth visibility into the behavior, performance, and decision-making processes of Large Language Models (LLMs). It focuses on understanding how and why LLMs produce certain outputs, enabling developers to identify issues, optimize performance,...

LLM Monitoring is the process of continuously tracking the performance, behavior, and outputs of Large Language Models (LLMs) during real-world deployment. This practice ensures that the models maintain reliability, relevance, and alignment with user expectations, while also detecting and addressing...

LLM Evaluation refers to the systematic assessment of Large Language Models (LLMs) to measure their performance, reliability, and alignment with desired objectives. This process employs a variety of metrics and methodologies to evaluate the quality of outputs, identify limitations, and...

An LLM Agent is an AI application that leverages the capabilities of a Large Language Model (LLM) to perform specific tasks autonomously. By combining the generative power of LLMs with logic, memory, and external data sources, LLM agents can act...

Large Language Model (LLM) is a type of AI model designed to process, understand, and generate human-like text. LLMs are trained on vast amounts of text data and use transformer-based architectures to excel in a wide range of natural language...

Latent space refers to the abstract, multidimensional space where data is represented in a compressed or encoded form. It is created by machine learning models, such as neural networks or autoencoders, to capture the most essential features of data, removing...

LangChain is a framework designed for building applications powered by Large Language Models (LLMs) that integrate external data sources, memory, and complex workflows. It allows developers to create sophisticated AI applications by chaining multiple LLM queries together and incorporating retrieval-based...

KorNAT (Korean National Alignment Test) is a benchmark dataset developed to assess the alignment of Large Language Models (LLMs) with South Korean social values and common knowledge. It evaluates models across two primary dimensions: Social Value Alignment: Measures how well...

A knowledge graph is a structured representation of information where entities (nodes) are connected by relationships (edges) to form a graph. It organizes and links data in a way that allows machines to understand, retrieve, and infer new information efficiently,...