ROUGE (Recall-Oriented Understudy for Gisting Evaluation) is a set of metrics used to evaluate the quality of automatically generated summaries by comparing them to human-written reference summaries. Instead of assessing a summary’s correctness or coherence directly, ROUGE measures how many...

RNN (Recurrent Neural Network) is a type of neural architecture designed to handle sequential data, such as time series, text, or speech. Unlike traditional neural networks, RNNs have loops that allow information to persist through multiple steps. This makes them...

RLHF (Reinforcement Learning from Human Feedback) is a technique that incorporates human judgments into the training loop of reinforcement learning models. Instead of using only automated reward signals, RLHF involves having humans provide guidance—such as ranking outputs or offering corrective...

Reinforcement Learning (RL) is a branch of machine learning in which an agent learns to make decisions by interacting with an environment. Instead of relying on labeled data, the agent explores possible actions and receives feedback in the form of...

Regression is a statistical and machine learning technique used to understand the relationship between a target variable and one or more independent variables. By fitting a function—often a line or a curve—to data points, regression allows us to predict continuous...

A Red Team is a group or methodology designed to rigorously test the security, effectiveness, and resilience of an organization’s systems, strategies, and operations. By adopting an adversarial perspective, the Red Team simulates realistic attacks or challenges, exposing vulnerabilities and...

Rectified Flow is a generative modeling technique aimed at improving how probability distributions are learned and represented. Building upon concepts from diffusion-based and score-based generative models, it employs a framework that “rectifies” or corrects the flow of probability density across...

RAGAS (Retrieval-Augmented Generation Assessment) is a framework and methodology for evaluating and assessing Retrieval-Augmented Generation (RAG) systems. While RAG focuses on integrating external knowledge sources to produce more informed and accurate responses, RAGAS takes it a step further by offering...

RAG (Retrieval Augmented Generation) is a technique that enhances generative AI models—such as large language models—by integrating external, domain-specific knowledge sources into the generation process. Rather than relying solely on patterns learned from pre-training, RAG dynamically fetches relevant information from...