Glossary
Glossary
AI terminology, defined concisely.
38 terms
A
- AI accelerator (NPU/TPU) Specialized chip for AI workloads — NPUs in phones, Google TPUs, AWS Trainium — often faster and more cost-efficient than GPUs per dollar spent.
- AI Act Article 50 (transparency) Article 50 of the EU AI Act sets transparency rules — chatbots, deepfakes, and AI-generated content must be clearly disclosed; enforcement begins Aug 2026.
- AI Agent An LLM-powered system that pursues a goal autonomously by planning, calling tools, and iterating on its own output until the task is complete.
- AI alignment The research field that aims to ensure AI systems follow human intent, values, and safety goals rather than pursuing unintended objectives.
- AI safety Broader field covering technical, organizational, and policy risks of AI systems — from mistakes and misuse to longer-term existential concerns.
- Attention mechanism A neural network technique that lets a model weigh the relevance of each input token to every other, forming the core of modern transformers.
C
- Chatbot A software agent that holds a conversation with a user via text or voice; modern chatbots are powered by large language models and tool integrations.
- Claude A family of large language models built by Anthropic with a focus on safety, long context, and tool use; powers Claude.ai and the Claude Code agent.
- Context window The maximum number of tokens an LLM can process at once — including prompt, documents, and answer; today ranges from 8K to 2 million tokens.
D
- Deep learning A branch of machine learning that uses multi-layered neural networks to learn complex patterns; powers modern vision, speech, and language AI systems.
- Diffusion model A class of generative models that learn to reverse a gradual noising process; the dominant approach for AI-generated images, video, and audio today.
E
- Embedding A vector representation of a word, sentence, or document in a high-dimensional space where semantically similar items have nearby vectors.
- EU AI Act The European Union's regulation governing AI systems by risk tier (unacceptable, high, limited, minimal); the world's first comprehensive AI law, with enforcement phasing in 2024-2027.
F
- Fine-tuning The process of further training a pre-trained language model on a smaller, task-specific dataset to specialize its behavior or domain knowledge.
- Foundation model A large model trained on broad data that can be adapted to many tasks; Stanford CRFM term covering LLMs, vision models, and multimodal systems.
G
- Generative Pre-trained Transformer (GPT) A family of decoder-only transformer language models pretrained on vast text and fine-tuned to follow instructions; the architecture behind ChatGPT and peers.
- Google Gemini A family of multimodal foundation models from Google DeepMind handling text, images, audio, and video; powers Gemini app, Workspace, and Vertex AI.
- Graphics Processing Unit (GPU) Graphics Processing Unit with thousands of parallel cores; today the dominant hardware for training and serving AI models, led by NVIDIA H100/B200.
H
I
K
L
- Large Language Model A neural network trained on vast text corpora to predict and generate human language; the foundation of modern AI assistants like ChatGPT, Claude, and Gemini.
- Llama (Meta) A family of open-weight large language models released by Meta, widely used as a base for fine-tuning and on-device deployment by the open-source community.
M
- Mixture of Experts (MoE) A neural network architecture that activates only a subset of its parameters for each input, providing the capability of a much larger model at a fraction of the inference cost.
- Model Context Protocol (MCP) An open protocol introduced by Anthropic in 2024 that standardizes how AI assistants connect to external tools and data sources, similar to how USB-C standardizes physical connections.
- Multi-agent system An AI architecture in which several specialised agents collaborate, delegate, or compete to solve a task more reliably than a single monolithic model could.
N
P
- Prompt engineering The practice of designing inputs to language models so they reliably produce the desired output; covers wording, structure, examples, and system prompts.
- Prompt Injection An attack where untrusted text in an LLM's input causes the model to follow attacker instructions rather than the developer's, ranked #1 in the OWASP Top 10 for LLM applications.
R
- Reasoning Model An LLM trained to produce a long, deliberate chain of thought before its final answer, trading inference time for accuracy on complex problems.
- Red team (AI) Structured adversarial testing of AI systems — prompt injection, jailbreaks, misuse — designed to surface vulnerabilities before production launch.
- Reinforcement Learning from Human Feedback (RLHF) A training technique in which human raters rank model responses, and those rankings are used to fine-tune an LLM toward helpfulness and safety.
- Retrieval-Augmented Generation (RAG) A pattern that combines a search/retrieval system with a language model: the model fetches relevant documents from a knowledge source before answering, grounding output in real data.
S
- Self-supervised learning Training approach where a model learns from unlabeled data by creating its own targets, such as predicting hidden tokens within a sentence.
- Stable Diffusion An open-weight latent diffusion model released by Stability AI in 2022; first widely available text-to-image generator runnable on consumer GPUs.
T
- Tokenization The process of splitting text into smaller units called tokens — words, subwords, or characters — that a language model can process numerically.
- Transformer The neural network architecture introduced in 2017 that powers virtually every modern large language model. Built around the self-attention mechanism.