Terminology

D

display title

The display title sets a page title that is different from the page name.

It becomes the preferred page label used in many automatically generated page lists. It can be changed with the visual editor (Options > Advanced settings). The source of the wiki page shows the magic word {{DISPLAYTITLE:My display title}}.

L

LLM

A large language model (LLM) is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation. The largest and most capable LLMs are generative pre-trained transformers (GPTs) and provide the core capabilities of chatbots such as ChatGPT, Gemini and Claude. wikipedia

R

RAG pipeline

Retrieval-augmented generation (RAG) is a technique that enables large language models (LLMs) to retrieve and incorporate new information. With RAG, LLMs do not respond to user queries until they refer to a specified set of documents. These documents supplement information from the LLM's pre-existing training data. This allows LLMs to use domain-specific and/or updated information that is not available in the training data. For example, this helps LLM-based chatbots access internal company data or generate responses based on authoritative sources. wikipedia

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The display title sets a page title that is different from the page name.

It becomes the preferred page label used in many automatically generated page lists. It can be changed with the visual editor (Options > Advanced settings). The source of the wiki page shows the magic word {{DISPLAYTITLE:My display title}}.

A large language model (LLM) is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation. The largest and most capable LLMs are generative pre-trained transformers (GPTs) and provide the core capabilities of chatbots such as ChatGPT, Gemini and Claude. wikipedia

Retrieval-augmented generation (RAG) is a technique that enables large language models (LLMs) to retrieve and incorporate new information. With RAG, LLMs do not respond to user queries until they refer to a specified set of documents. These documents supplement information from the LLM's pre-existing training data. This allows LLMs to use domain-specific and/or updated information that is not available in the training data. For example, this helps LLM-based chatbots access internal company data or generate responses based on authoritative sources. wikipedia