AI — Glossary & Terminology

Published

March 5, 2024

Modified

September 24, 2025

Prompt

Input text/query from a user…

…starting point for the model’s generation process
…sets the context for the response

Tokens

Individual units of text that the model processes

For example: words, characters, punctuation, spaces
Tokenization — Process of converting text into tokens
- …tokenisation in language dependent and varies between models
- …symbols are then mapped (embedded) to vectors (feed to the model)
Limits…
- …models have different limits for input and output tokens
- …prompt engineering may include the necessity to condense the input
- …requests to different models are priced by counting tokens

Model

A model (hypothesis) is the output of a machine learning algorithm

Trained on vast amounts of text data…
- …a specific representation learned from data…
- …by undergoing a training process driven by (huge amounts of) input data
- …contains learned patterns evaluated from the input data
- …contains guidelines for making predictions
Structure of the model defines how it processes information

Parameters

Internal variables a model learns during training

Model’s performance is often measured by the number of parameters
Larger models (more parameters) capture more complex language patterns

Training

Based on a set of inputs (features) with expected outputs (labels)

…basically the concept of learning from examples to make future predictions
…process to algorithmically recognize patterns & relationships from datasets
Building block of a machine learning model…
…where the quality of the input dataset has significant impact on the model capabilities
Input features are individual measurable property of data…
- …represented as a set of numeric values in a features vector
- Feature vector used as input to the machine learning model during training
- A feature extractor is program to extract relevant features from input data

Prediction created by a trained model based on features extracted from (unseen) input date

Label

Data labels (data annotation) …values to be predicted by a model…

…essential to supervised training of a machine learning model
Labels describe attributes & characteristics of a data point…
…can be based on class, subject, theme, or other categories
Example…
- …images with corresponding labels to indicting visible objects in the picture
- …image recognition then learns to recognize patterns for a labeled object

RAG

RAG (Retrieval-Augmented Generation) enhances LLMs

“If an LLM servers as brain, RAG is the library to the brain”
Goal …improving factual accuracy …reducing hallucinations
Ground generated response in current data…
- …retrieved in real-time in relation to the user prompt…
- …rather than relying solely on the model’s pre-existing training data
Enables to add domain-specific knowledge tailored to a specific environment

Workflow (first-stage pipeline)…

Retrieval, incorporate…
- …relevant, external, up-to-date information
- …from trusted data sources (documents, databases)
Argumentation …combine selected data with user input …feed to LLM inference
Generation …generate response based on this enriched context

MLLM

Multimodal Large Language Models (MLLMs)

Multiple modalities — vision, language, audio, etc
Process & reason across multiple modalities
Example: combine image + text prompts to guide responses
Combining multiple domain specific models…
- …for example vision encoder (images) & language model (text)
- …with a projection layer to map models on a common token space

Example for multimodal reasoning…

GTP-4V (2023) GPT-4 with vision, OpenAI
LLaVA (2023), Open Source
BakLLaVA (2024)

AI Agents

LLM agents …agentic technology

Autonomous agent that acts on behalf of users…
- …to plan & orchestrate tasks
- …using an LLM to communicate

MCP

MCP (Model Context Protocol)

Standardizes how AI agents access/manipulate external tools

--- title: AI — Glossary & Terminology categories: - AI date: 2024/03/05 date-modified: 2025/09/24 toc-expand: 3 --- ### Prompt Input text/query from a user… - …starting point for the model’s generation process - …sets the context for the response ### Tokens **Individual units of text that the model processes** - For example: words, characters, punctuation, spaces - **Tokenization** — Process of converting text into tokens - …tokenisation in language dependent and varies between models - …symbols are then mapped (embedded) to vectors (feed to the model) - Limits… - …models have different limits for input and output tokens - …prompt engineering may include the necessity to condense the input - …requests to different models are priced by counting tokens ### Model A model (hypothesis) is the output of a machine learning algorithm - Trained on vast amounts of text data… - …a specific representation learned from data… - …by undergoing a training process driven by (huge amounts of) input data - …contains learned patterns evaluated from the input data - …contains guidelines for making predictions - Structure of the model defines how it processes information ### Parameters **Internal variables a model learns during training** - Model’s performance is often measured by the number of parameters - Larger models (more parameters) capture more complex language patterns ### Training Based on a set of inputs (features) with expected outputs (labels) - …basically the concept of learning from examples to make future predictions - …process to algorithmically recognize patterns & relationships from datasets - Building block of a machine learning model… - …where the quality of the input dataset has significant impact on the model capabilities - Input features are individual measurable property of data… - …represented as a set of numeric values in a features vector - **Feature vector** used as input to the machine learning model during training - A feature extractor is program to extract relevant features from input data **Prediction** created by a trained model based on features extracted from (unseen) input date ### Label Data **labels** (data annotation) …values to be predicted by a model… - …essential to supervised training of a machine learning model - Labels describe attributes & characteristics of a data point… - …can be based on class, subject, theme, or other categories - Example… - …images with corresponding labels to indicting visible objects in the picture - …image recognition then learns to recognize patterns for a labeled object ### RAG RAG (Retrieval-Augmented Generation) enhances LLMs - _"If an LLM servers as brain, RAG is the library to the brain"_ - Goal …improving factual accuracy …reducing hallucinations - **Ground** generated response in current data… - …retrieved in real-time in relation to the user prompt… - …rather than relying solely on the model's pre-existing training data - Enables to add domain-specific knowledge tailored to a specific environment Workflow (first-stage pipeline)… - **Retrieval**, incorporate… - …relevant, external, up-to-date information - …from trusted data sources (documents, databases) - **Argumentation** …combine selected data with user input …feed to LLM inference - **Generation** …generate response based on this enriched context ### MLLM Multimodal Large Language Models (MLLMs) - Multiple modalities — vision, language, audio, etc - Process & reason across multiple modalities - Example: combine image + text prompts to guide responses - Combining multiple domain specific models… - …for example vision encoder (images) & language model (text) - …with a **projection layer** to map models on a common token space Example for multimodal reasoning… - GTP-4V (2023) GPT-4 with vision, OpenAI - LLaVA (2023), Open Source - BakLLaVA (2024) ### AI Agents LLM agents …agentic technology - Autonomous agent that acts on behalf of users… - …to plan & orchestrate tasks - …using an LLM to communicate ### MCP MCP (Model Context Protocol) Standardizes how AI agents access/manipulate external tools