Concept graph
Generative AI and LLM glossary
Clear AI definitions with practical context, examples, and links to related models, tools, and concepts.
Machine Learning
domain-adaptation
A technique in machine learning that aims to improve model performance on a target domain by leveraging labeled data from a related source domain.
Regularization
Dropout
A regularization technique used to prevent overfitting in neural networks by randomly deactivating a fraction of neurons during training.
AI Safety and Governance
Explainability
Explainability describes how clearly people can understand why an AI system produced a particular output or decision.
robotics
explainable-robotics
A field of study focused on making robotic systems understandable and transparent to users.
Data Analysis
exploratory-data-analysis
An approach to analyzing data sets to summarize their main characteristics.
Evaluation
Faithfulness
Faithfulness is whether every claim in a generated answer is supported by the supplied evidence.
Data Processing
feature-extraction
The process of transforming raw data into a set of usable characteristics for model training.
Data Processing
feature-selection
The process of selecting a subset of relevant features for model training.
Machine Learning
Federated Learning
A machine learning approach that allows models to be trained across decentralized devices or servers holding local data samples.
Machine Learning
few-shot-learning
A machine learning paradigm that trains models with very few labeled examples.
Training
Fine-tuning
Fine-tuning continues training a pretrained model on a smaller, task-specific dataset so it follows that task more reliably.
Neural Networks
Generative Adversarial Network (GAN)
A generative adversarial network (GAN) trains a generator and a discriminator in opposition so the generator learns to produce samples that resemble a training distribution.
Artificial Intelligence
Generative AI
AI systems that can create new content, such as text, images, or music.
Evaluation
Golden set
A golden set is a versioned collection of labeled tasks used to score models and prompts against the behavior you actually want.
Retrieval
Grounding
Grounding means tying model claims to retrieved documents, tools, or other checkable evidence instead of parametric memory alone.
Machine Learning
Hyperparameter tuning
Hyperparameter tuning searches training or decoding settings that are not learned as weights, such as learning rate or temperature.
Machine Learning
incremental-learning
A machine learning approach that updates models continuously with new data without retraining from scratch.
Models
Indic LLM
An Indic LLM is a language model optimized for Indian languages, scripts, romanized text, code-mixing, and India-specific cultural or domain context.
Agent security
Indirect prompt injection
Indirect prompt injection happens when untrusted external content, such as a webpage, email, document, or tool result, contains instructions that try to steer an AI system.
Serving
Inference
Inference is running a trained model on new input to produce an output. Training updates the model's weights. Inference does not.
Data Management
information-retrieval
The process of obtaining information system resources that are relevant to an information need.
Data Representation
Knowledge Graph
A structured representation of knowledge that connects entities and their relationships, typically used in AI for information retrieval.
Models
Large language model
A large language model, or LLM, is a neural text model trained on large corpora to predict, generate, transform, and reason over language and code.
Evaluation
LLM evaluation
LLM evaluation measures whether a model or AI workflow is accurate, useful, safe, reliable, and cost-effective for a target task.