AI Glossary
The definitive dictionary for AI, Machine Learning, and Governance terminology. From Flash Attention to RAG — look up any term.
R
RAG Pipeline
The complete end-to-end system for retrieval-augmented generation, including document ingestion, chunking, embedding, indexing, retrieval, reranking, prompt construction, and generation.
Random Forest
An ensemble learning method that builds multiple decision trees during training and outputs the majority vote (classification) or average prediction (regression) of all the trees. The 'forest' of diverse trees is more robust than any single tree.
Reasoning
An AI model's ability to think logically, make inferences, draw conclusions, and solve problems that require multi-step thought. Reasoning goes beyond pattern matching to genuine logical analysis.
Recall
Of all the actually positive items in the dataset, the proportion that the model correctly identified. Recall measures how completely the model finds all relevant items.
Recommendation System
An AI system that predicts and suggests items a user might be interested in based on their behavior, preferences, and similarities to other users.
Recurrent Neural Network
A type of neural network designed for sequential data where the output at each step depends on previous steps. RNNs have a form of memory that allows them to process sequences like text, time series, and audio.
Red Teaming
The practice of systematically testing AI systems by attempting to find failures, vulnerabilities, and harmful behaviors before deployment. Red teamers actively try to break the system.
Regression
A type of supervised learning task where the model predicts a continuous numerical value rather than a discrete category. The output can be any number within a range.
Regularization
Techniques used to prevent overfitting by adding constraints or penalties to the model during training. Regularization discourages the model from becoming too complex or fitting noise in the training data.
Reinforcement Learning
A type of machine learning where an agent learns to make decisions by taking actions in an environment and receiving rewards or penalties. The agent aims to maximize cumulative reward over time through trial and error.
Reinforcement Learning from AI Feedback
A variant of RLHF where AI models (instead of humans) provide the feedback used to train reward models and align language models. RLAIF reduces the cost and scalability constraints of human feedback.
Relation Extraction
The NLP task of identifying and classifying semantic relationships between entities mentioned in text. It extracts structured facts from unstructured text.
ReLU
Rectified Linear Unit — the most commonly used activation function in deep learning. It outputs the input directly if positive, and zero otherwise: f(x) = max(0, x).
Representation Learning
The process of automatically discovering useful features or representations from raw data, rather than manually engineering them. Deep learning excels at learning hierarchical representations.
Reranking
A second-stage ranking process that takes initial search results and reorders them using a more sophisticated model. Reranking improves precision by applying deeper analysis to a smaller candidate set.
Residual Connection
A shortcut that allows the input to a layer to bypass one or more layers and be added directly to the output. This enables training of much deeper networks by ensuring gradient flow.
Responsible AI
An approach to developing and deploying AI that prioritizes ethical considerations, fairness, transparency, accountability, and societal benefit throughout the entire AI lifecycle.
Responsible AI Framework
A structured set of principles, policies, processes, and tools that guide an organization's AI development and deployment to ensure ethical, fair, and beneficial outcomes.
Responsible Disclosure
The practice of reporting AI vulnerabilities, biases, or safety issues to the appropriate parties before making them public, giving developers time to fix issues before they can be exploited.
Responsible Scaling
A policy framework where AI developers commit to implementing specific safety measures as their models become more capable, with defined capability thresholds triggering additional safeguards.
Retraining
The process of training a model again on updated data to restore or improve its performance. Retraining addresses model drift and incorporates new patterns the original model did not learn.
Retrieval
The process of finding and extracting relevant information from a large collection of documents or data in response to a query. In AI systems, retrieval is often the first step before generation.
Retrieval Evaluation
Methods for measuring how well a retrieval system finds relevant documents. Key metrics include recall at K, mean reciprocal rank, and normalized discounted cumulative gain.
Retrieval Latency
The time it takes for a retrieval system to search through stored documents or embeddings and return relevant results. Measured in milliseconds, it is a critical component of RAG system performance.
Retrieval Quality
A measure of how relevant and accurate the documents retrieved by a search or RAG system are relative to the user's query. Poor retrieval quality is the leading cause of RAG failures.
Retrieval-Augmented Fine-Tuning
Combining fine-tuning with retrieval capabilities, training a model to effectively use retrieved context. RAFT teaches the model when and how to leverage external knowledge.
Retrieval-Augmented Generation
A technique that enhances LLM outputs by first retrieving relevant information from external knowledge sources and then using that information as context for generation. RAG combines the power of search with the fluency of language models.
Retrieval-Augmented Reasoning
An advanced approach where an AI model interleaves retrieval with reasoning steps, fetching new information mid-reasoning rather than retrieving everything upfront.
Reward Hacking
When an AI system finds unintended ways to maximize its reward signal that do not align with the designer's actual goals. The system technically optimizes the metric but violates the spirit of the objective.
Reward Model
A model trained to predict how good a response is based on human preferences. In RLHF, the reward model scores outputs to guide the language model toward responses humans prefer.
Reward Modeling
Training a separate model to predict human preferences, which then serves as the reward signal for reinforcement learning. The reward model learns what humans consider 'good' responses.
Reward Shaping
The practice of designing intermediate rewards to guide a reinforcement learning agent toward desired behavior, rather than only providing reward at the final goal state.
Risk Assessment
The systematic process of identifying, analyzing, and evaluating potential risks associated with an AI system. Risk assessment considers both the likelihood and impact of potential harms.
RLHF
Reinforcement Learning from Human Feedback — a technique used to align language models with human preferences. Human raters rank model outputs, and this feedback trains a reward model that guides further training.
Robustness
The ability of an AI model to maintain reliable performance when faced with unexpected inputs, adversarial attacks, data distribution changes, or edge cases.
Role Prompting
A technique where the model is instructed to adopt a specific persona, expertise, or perspective in its responses. The assigned role shapes tone, depth, terminology, and reasoning approach.