AI Glossary: Letter "R"

Retrieval-Augmented Generation (RAG) is a methodology that optimizes the output of a Large Language Model (LLM) by referencing an authoritative, external knowledge base or Vector Database before generating a response. RAG helps models access real-time information and drastically reduces hallucination.

Random Forest is an ensemble supervised learning algorithm composed of many individual Decision Trees that work together. It trains trees on random subsets of the data and features, averaging their predictions for output.

A Reasoning Model (or o1-style model) is an artificial intelligence model trained to perform reinforcement learning and execute chain-of-thought steps internally before returning an answer. This allows the model to deliberate, correct mistakes, and evaluate strategies.

Recall (Sensitivity or True Positive Rate) is a classification evaluation metric measuring the fraction of actual positive examples that the model correctly identified, calculated as true positives divided by all actual positives.

A Recurrent Neural Network (RNN) is a class of artificial neural network where connections between nodes form a directed graph along a temporal sequence, allowing it to exhibit temporal dynamic behavior and process variable-length inputs.

Reinforcement Learning (RL) is a machine learning training paradigm where an agent learns to make decisions by performing actions in an environment to maximize cumulative rewards. The agent learns through trial-and-error feedback.

Rejection Sampling (in LLMs) is a data curation technique where a generator model produces multiple candidate answers, and a separate evaluator model filters out low-quality outputs. The remaining high-quality responses are then used for supervised fine-tuning.

Repository Intelligence is a capability in AI developer tooling that allows models to index, analyze, and reason over an entire software codebase structure, rather than just reading active, isolated files.

Representation Learning is a set of techniques in machine learning that allows a system to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering, enabling neural networks to learn hierarchical abstractions directly.

Reranking is a secondary step in RAG (Retrieval-Augmented Generation) pipelines where a highly accurate model evaluates and re-orders the candidate documents fetched during initial vector search, ensuring the most relevant context is placed at the top.

A Residual Connection (or skip connection) is an architectural feature in deep neural networks that passes the input of a layer directly to its output, bypassing one or more intermediate layers by adding them together.

Responsible AI is a business governance framework that guides how an organization designs, develops, and deploys artificial intelligence systems ethically, ensuring transparency, fairness, privacy, safety, and accountability.

Retrieval Precision is an evaluation metric in RAG systems measuring the fraction of retrieved document chunks that are actually relevant to answering the user query. High retrieval precision prevents prompt clutter and distraction.

Retrieval Recall is a search metric measuring the percentage of relevant documents successfully retrieved from a database relative to all existing relevant documents. High recall ensures the LLM receives all context needed to answer a query.

A Reward Function is a mathematical formula that defines the goal in reinforcement learning by assigning a numerical score to the states and actions of an agent based on their desirability.

A Reward Model is a neural network trained to score responses generated by an LLM based on human preferences (e.g., helpfulness, safety, format correctness). It is used as the scoring engine in reinforcement learning loops like RLHF.

Reinforcement Learning from AI Feedback (RLAIF) is a model alignment technique where human evaluators are replaced by an AI model (the judge) to generate preference labels for training, lowering alignment training costs.

Reinforcement Learning from Human Feedback (RLHF) is a training methodology used to align LLMs with human values and preferences. It uses human evaluations to train a reward model, which then guides the LLM to generate helpful, harmless, and honest outputs.

Rotary Position Embedding (RoPE) is an advanced position encoding method applying rotation matrices to token vectors, naturally capturing relative distance between tokens.