Terms starting with L

12 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.

LLM (Large Language Model)
Models & inference
A language model with enough capacity and broad training to perform many language tasks through prompting or adaptation. Most current LLMs use transformer architectures and sequence-prediction objectives, but size thresholds, data sources, and training recipes vary.
LLM-as-a-Judge
Evaluation & safety
Using a language model to score, compare, classify, or critique another system's output against a rubric.
Late Fusion
Multimodal systems
Processing modalities through separate encoders or predictors and combining their high-level representations, scores, or decisions near the task output.
Latent Space
Data & representations
A learned representation space whose coordinates encode factors useful to a model. It may be lower-dimensional than the input, but compression is not required for every latent representation.
Learning Rate
Math & training
A scale factor used by an optimizer to control parameter-update magnitude. Values that are too large can destabilize training; values that are too small can make useful progress impractically slow.
Learning Rate Schedule
Math & training
A policy that changes the optimizer's learning rate as training progresses according to steps, epochs, metrics, or a predefined curve.
Least Privilege
Evaluation & safety
Giving a model, agent, tool, or user only the permissions required for the current task, for only as long as those permissions are needed.
LoRA (Low-Rank Adaptation)
Math & training
A method that keeps base weights frozen and learns low-rank update matrices for selected layers. It reduces the number of trainable parameters and can lower training memory relative to full-parameter fine-tuning.
Load Shedding
Reliability & operations
Deliberately rejecting, dropping, or cancelling selected work at one or more overload boundaries when demand exceeds the capacity available to produce useful results.
Logits
Models & inference
The model's unnormalized numeric scores for candidate outcomes before a normalization function or decoding rule converts them into selections.
Loss Function
Math & training
An objective that maps predictions and targets, sometimes with regularization terms, to a value optimization tries to reduce. The loss determines which errors training directly rewards or penalizes.
Lost in the Middle
Prompting & context
A long-context failure pattern in which model performance changes with evidence position and can degrade when relevant information sits between the beginning and end.