AI Fundamentals | AI, DX & Security Glossary
Glossary terms in "AI Fundamentals" — practical definitions on AI, DX, and security for executives and IT teams, with diagrams.
Open-weight model
An open-weight model is a language model whose trained weights (parameters) are publicly released an
Inference-time Scaling (Test-time Compute)
Inference-time scaling is a technique that dynamically increases or decreases the amount of computat
Reasoning Model
A type of large language model that generates an explicit chain of thought before responding, solvin
Sparse Model
A Sparse Model is a general term for neural network architectures that activate only a subset of the
TurboQuant
A memory compression technology for LLMs developed by Google. It reduces memory consumption by up to
Knowledge Distillation (Knowledge Distillation)
A technique that transfers knowledge from a large teacher model to a small student model, creating a
Dense Model (Tightly Coupled Model)
A Dense Model is a neural network architecture in which all of the model's parameters are used for c
Speculative Decoding
A inference acceleration technique in which a small draft model proposes multiple tokens speculative
Token (Token)
A token is the smallest unit used by an LLM when processing text. It is not necessarily a whole word
BPE Tokenizer (Byte-Pair Encoding Tokenizer)
An algorithm that merges text based on frequent patterns and splits it into subword units. It direct
Base Model (Foundation Model)
A base model (Foundation Model) is a general-purpose AI model pre-trained on large-scale datasets. R
Quantization (Quantization)
An optimization technique that compresses model size by reducing parameter precision from 16-bit to
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