Terms starting with G

10 of 250 glossary terms. Each entry links to its own page with why it matters and where it fits.

Definitions here are written for engineers, not grammarians: what the term means, why it matters when it comes up, and where it sits in the curriculum — most term pages name the lessons that lean on the concept, so you can jump from a definition straight to the teaching. Use the letter bar below to move through the alphabet, or press the slash key to search the whole corpus directly.

Every entry carries three things besides the definition: the plain aliases it travels under (listed as “also”), a category tag for which stretch of the curriculum it belongs to, and the cross-references that place it next to its neighbors — the terms you would meet in the same lesson. A term earns a page here only when a lesson in the curriculum actually uses it, so the corpus stays the shape of the material instead of the shape of a dictionary.

One honest way to use this page: read down the list of entries below and note every term you could not define in a sentence if a colleague asked. That list — not the whole letter — is your revision list, and each entry links to the reading that closes the gap.

GAN (Generative Adversarial Network)
Models & inference
A generator network tries to create realistic data while a discriminator network tries to tell real from fake. They train together: the generator gets better at fooling the discriminator, and the discriminator gets better at detecting fakes.
GPT
Models & inference
Generative Pre-trained Transformer, a family label for generative transformer models pretrained on sequence-prediction objectives and adapted for downstream use. Product names and model architectures should not be treated as interchangeable.
Goodput
Infrastructure & serving
The rate of completed requests that satisfy defined service constraints, such as both time-to-first-token and per-token latency objectives, under a stated workload.
Graceful Degradation
Reliability & operations
Preserving a bounded core service when capacity or dependencies are impaired by reducing optional quality, features, freshness, or workload instead of failing every request.
Gradient
Math & training
A vector of partial derivatives pointing in the direction of steepest increase. In ML, you go opposite to the gradient (gradient descent) to minimize the loss.
Gradient Accumulation
Math & training
Summing or averaging gradients from several microbatches before performing one optimizer update.
Gradient Clipping
Math & training
Limiting gradient values or their combined norm before an optimizer update when they exceed a chosen threshold.
Gradient Descent
Math & training
A family of optimization updates that move parameters using the negative gradient of an objective, usually estimated from batches rather than the entire dataset.
Grounding
Retrieval & generation
Connecting a generated answer or action to evidence, state, or observations that the system can identify and check.
Guardrails
Evaluation & safety
System controls that constrain inputs, tool use, outputs, permissions, and escalation. They can include schemas, policy checks, classifiers, allowlists, sandboxing, approvals, and post-action verification.