What is a constructed language?

Stephen M. Walker II · Co-Founder / CEO

What is a constructed language?

A constructed language, often shortened to conlang, is a language whose phonology, grammar, and vocabulary are consciously devised for a specific purpose, rather than having developed naturally. This purpose can range from facilitating international communication, adding depth to a work of fiction, experimenting in linguistics or cognitive science, creating art, or even for language games.

Constructed languages can be categorized into three main types: auxiliary languages (auxlangs), engineered languages (engelangs), and artistic languages (artlangs). Auxlangs are created to improve communication within a community, engelangs are devised as an experiment, often to demonstrate the complexity of language creation, adoption, or adaptation, and artlangs are invented by writers or authors to add depth to a fictional world.

Examples of constructed languages include Esperanto, Klingon, and Dothraki. Esperanto, created in the late 19th century by Polish ophthalmologist L. L. Zamenhof, is the most successful constructed language in terms of the number of speakers. Klingon and Dothraki, on the other hand, were created for the fictional universes of Star Trek and Game of Thrones, respectively.

In the context of artificial intelligence, a constructed language can be used to help machines communicate with each other. These languages are typically designed to be efficient and expressive communication tools for AI agents.

The process of creating a constructed language often involves taking inspiration from the grammar, vocabulary, and phonology of one or more natural languages, or it can be formed completely from scratch. The creator of a constructed language is known as a conlanger.

More terms

Continue exploring the glossary.

Learn how teams define, measure, and improve LLM systems.

Glossary term

What is Monte Carlo tree search?

Monte Carlo tree search (MCTS) is an intelligent search algorithm that combines elements of random sampling, simulation, and tree exploration to efficiently explore a large decision space. It has been widely used in games like Go, chess, and poker, as well as other complex domains where traditional search methods may be too slow or computationally expensive.
Read term

Glossary term

What Are Multi-Task Learning Models in AI?

Multi-Task Learning Models in AI are designed to handle multiple learning tasks simultaneously, leveraging commonalities and differences across tasks to improve the performance of all tasks. They are used in various domains like natural language processing, computer vision, and speech recognition.
Read term

It's time to build

Collaborate with your team on reliable Generative AI features.
Want expert guidance? Book a 1:1 onboarding session from your dashboard.

Talk to sales