AI Glossary
The definitive dictionary for AI, Machine Learning, and Governance terminology. From Flash Attention to RAG — look up any term.
G
GDPR
General Data Protection Regulation — the European Union's comprehensive data protection law that gives individuals control over their personal data and imposes strict obligations on organizations handling that data.
Gemini
Google DeepMind's family of multimodal AI models designed to understand and generate text, code, images, audio, and video. Gemini is Google's flagship AI model series.
Generalization
A model's ability to perform well on new, unseen data that was not part of its training set. Generalization is the ultimate goal of machine learning — learning patterns, not memorizing examples.
Generative Adversarial Network
A framework where two neural networks compete — a generator creates fake data and a discriminator tries to tell real from fake. This adversarial process drives both networks to improve, producing increasingly realistic outputs.
Generative AI
AI systems that can create new content — text, images, music, code, video — rather than just analyzing or classifying existing data. These models learn patterns from training data and generate novel outputs that resemble the original data.
GGUF
A file format for storing quantized language models designed for efficient CPU inference. GGUF is the standard format used by llama.cpp and is popular for local LLM deployment.
Google DeepMind
Google's AI research division, formed by merging Google Brain and DeepMind in 2023. Known for AlphaGo, AlphaFold, and the Gemini model family.
GPT
Generative Pre-trained Transformer — a family of large language models developed by OpenAI. GPT models are trained to predict the next token in a sequence and can generate coherent, contextually relevant text across many tasks.
GPU
Graphics Processing Unit — originally designed for rendering graphics, GPUs excel at the parallel mathematical operations needed for training and running AI models. They are the primary hardware for modern AI.
Gradient Accumulation
A technique that simulates larger batch sizes by accumulating gradients over multiple forward passes before performing a single weight update. This enables large effective batch sizes on limited hardware.
Gradient Boosting
An ensemble technique that builds models sequentially, where each new model focuses on correcting the errors made by previous models. It combines many weak learners into a single strong learner.
Gradient Clipping
A technique that caps gradient values at a maximum threshold during training to prevent exploding gradients. If a gradient exceeds the threshold, it is scaled down.
Gradient Descent
An optimization algorithm used to minimize the error (loss) of a model by iteratively adjusting parameters in the direction that reduces the loss most quickly. It is the primary method for training machine learning models.
Graph Neural Network
A type of neural network designed to operate on graph-structured data (nodes and edges). GNNs learn representations of nodes, edges, or entire graphs by aggregating information from neighbors.
GraphRAG
A RAG approach that uses knowledge graphs rather than vector databases for retrieval. It combines graph traversal with LLM generation to answer questions requiring multi-hop reasoning.
Greedy Decoding
A simple text generation strategy where the model always selects the most probable next token at each step. It is fast but can produce repetitive or suboptimal outputs.
Grounding
The practice of connecting AI model outputs to verifiable sources of information, ensuring responses are based on factual data rather than the model's potentially unreliable internal knowledge.
GRU
Gated Recurrent Unit — a simplified version of LSTM that uses fewer gates and parameters while achieving similar performance on many sequence tasks. It is faster to train than LSTM.
Guardrail Model
A separate, specialized AI model that monitors the inputs and outputs of a primary LLM to detect and block harmful, off-topic, or policy-violating content.
Guardrails
Safety mechanisms and constraints built into AI systems to prevent harmful, inappropriate, or off-topic outputs. Guardrails can operate at the prompt, model, or output level.