Concept graph
Generative AI and LLM glossary
Clear AI definitions with practical context, examples, and links to related models, tools, and concepts.
machine learning
out-of-distribution-generalization
The ability of a model to perform well on unseen data that is different from the training set.
Data Analysis
Outlier Detection
The identification of data points that deviate significantly from the majority of data.
Modeling
Overfitting
A modeling error that occurs when a model learns noise and details from the training data.
data-preprocessing
pipeline
A sequence of data processing steps for machine learning workflows.
evaluation
precision-recall-curve
A graphical representation of a model's trade-off between precision and recall.
Data Analysis
predictive-analytics
The use of statistical techniques to forecast future events based on historical data.
Data Science
Principal Component Analysis (PCA)
A dimensionality reduction technique used to simplify datasets while preserving variance.
Optimization
Prompt caching
Prompt caching reuses a previously processed prompt prefix so repeated requests with the same context can be faster and cheaper.
Product
Prompt engineering
Prompt engineering is the practice of writing instructions, examples, and output contracts so a model behaves reliably for a task.
Safety
Prompt injection
Prompt injection is an attack or failure mode where untrusted text tries to override system instructions or steer a model into unsafe behavior.
Machine Learning
pruning
The process of removing unnecessary parameters from a neural network to create a more efficient model.
Inference
Quantization
Quantization stores weights or activations in lower-precision formats such as 8-bit or 4-bit to reduce memory and often cost.
Computing
Quantum Computing
A type of computing that utilizes quantum mechanics to process information at unprecedented speeds.
Quantum Physics
quantum-entanglement
A physical phenomenon occurring when pairs or groups of particles interact in such a way that the quantum state of each particle cannot be described independently.
quantum computing
quantum-machine-learning
An interdisciplinary approach merging quantum computing with machine learning techniques.
Retrieval
RAG
RAG, retrieval-augmented generation, retrieves evidence first, then generates an answer from that evidence instead of parametric memory alone.
Inference
Reasoning effort
Reasoning effort is a model setting or design choice that controls how much reasoning budget the model spends before answering.
Disaster Recovery
recovery-point-objective
A metric that defines the maximum acceptable amount of data loss measured in time for a system after a failure.
Deep Learning
Recurrent Neural Network (RNN)
A type of neural network designed for sequential data processing.
Modeling
Regularization
A technique used to prevent overfitting by adding a penalty to the loss function.
Machine Learning
Reinforcement Learning
A type of machine learning focused on teaching agents to make decisions by maximizing cumulative rewards.
Training
RLHF
RLHF, reinforcement learning from human feedback, trains a model to prefer outputs that raters rank higher, usually via a reward model.
Automation
robotic process automation
Robotic process automation (RPA) is the use of software robots to automate repetitive tasks.
Engineering
robotics
The interdisciplinary field that combines engineering, computer science, and more to design and build robots.