Ground truth refers to the accurate, verified data used as a benchmark or standard to evaluate and train machine learning models. It represents the true state or label of data, often collected through expert annotation, observation, or measurement, and is...

Graph Retrieval-Augmented Generation (Graph RAG) is an advanced methodology in AI that combines retrieval-augmented generation systems with graph-based data structures, such as knowledge graphs or graph databases. This approach enhances large language models (LLMs) by incorporating structured, relational data to...

GPT (Generative Pre-trained Transformer) is a type of large language model developed by OpenAI that uses a transformer-based architecture to generate human-like text. It is trained on vast amounts of text data in an unsupervised manner, learning patterns, context, and...

A golden dataset is a high-quality, meticulously curated dataset used as a benchmark or standard for evaluating, validating, and training machine learning models. It is often considered the “gold standard” for a specific task, containing accurate, consistent, and representative data...

Generative AI refers to artificial intelligence systems designed to create new content, such as text, images, audio, video, or other data, by learning patterns from existing datasets. These systems use generative models to simulate creativity and produce outputs that are...

A Gaussian Diffusion Model is a type of generative model that creates data by simulating the process of adding and removing Gaussian noise to and from a dataset over a series of steps. The model learns to reverse this noise...

A Generative Adversarial Network (GAN) is a class of machine learning models designed for generative tasks, where the goal is to create new data that mimics the characteristics of a given dataset. A GAN consists of two neural networks—a generator...

G-eval is a framework or methodology used to evaluate the performance, reliability, and accuracy of generative AI models, such as those used in text, image, or audio generation. It focuses on assessing outputs for quality, coherence, creativity, and adherence to...

Fine-tuning is the process of adapting a pre-trained machine learning model to a specific task by continuing its training on a smaller, task-specific dataset. It builds upon the general knowledge already learned by the model during pre-training, allowing it to...

Few-shot learning is a machine learning approach where a model is trained to make accurate predictions or perform tasks using only a small number of labeled examples. This technique is particularly valuable in scenarios where collecting large amounts of labeled...