Concept graph
Generative AI and LLM glossary
Clear AI definitions with practical context, examples, and links to related models, tools, and concepts.
Optimization
Prompt caching
Prompt caching reuses a previously processed prompt prefix so repeated requests with the same context can be faster and cheaper.
Inference
prompt engineering
Prompt engineering is the practice of structuring instructions, context, and formats to get reliable model behavior.
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.
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.
Inference
RAG
RAG retrieves relevant passages (often via embeddings) and conditions generation on them—reducing reliance on 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.
Deep Learning
recurrent-neural-network
A class of neural networks designed for processing sequences of data.
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.
Machine Learning
reinforcement-learning-from-human-feedback
An approach in reinforcement learning where human feedback is used to shape agent learning and decision-making.
Training
retrieval augmented generation
Retrieval-augmented generation (RAG) grounds answers on retrieved documents instead of parametric memory alone.
Training
RLHF
RLHF aligns a model to human preferences using a reward model and reinforcement-style optimization.
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.
Quality Assurance
robotics-testing
Evaluation processes to ensure the functionality and safety of robotic systems.
Model Evaluation
Robustness
The ability of a model to maintain performance despite variations in input data or conditions.
Model Evaluation
Robustness Testing
Evaluating how well a model performs under various adversarial conditions.
Data Analysis
root-cause-analysis
The process of identifying the primary cause of a problem or defect.
reinforcement-learning
safe-exploration
A strategy in reinforcement learning that aims to balance exploration and exploitation while minimizing risks.