A Comprehensive Glossary of Key AI Terms for 2023

Jul 3, 2026

AI Summary

As artificial intelligence continues to evolve, a new vocabulary is emerging to describe its concepts and technologies. This glossary provides clear definitions of essential AI terms, helping individuals understand and navigate the rapidly changing landscape of AI applications and discussions.

Artificial intelligence is creating a new language with terms like LLMs, RAG, and RLHF, which can be confusing even for tech experts.
The glossary aims to provide straightforward definitions of AI terms for those involved in building, investing, or following AI developments.
Artificial general intelligence (AGI) refers to AI systems that can outperform humans in most tasks, with varying definitions from different organizations.
An AI agent is a tool that utilizes AI to perform complex tasks autonomously, differing from basic chatbots.
API endpoints act as interfaces that allow software applications to communicate and perform tasks, enabling automation through AI agents.
Chain-of-thought reasoning in AI involves breaking down problems into smaller steps to enhance accuracy, particularly in logic and coding.
A coding agent is a specialized AI that can autonomously write, test, and debug code, functioning like a highly efficient intern.
Compute refers to the computational power necessary for AI models to operate, relying on hardware like GPUs and CPUs.
Deep learning algorithms utilize artificial neural networks to make complex correlations and improve outputs through repetition and data.
Diffusion technology is used in generative AI models to create data by learning to reverse the process of data destruction.
Distillation extracts knowledge from larger AI models to create smaller, more efficient versions, often used to enhance performance.
Fine-tuning involves further training an AI model on specialized data to optimize it for specific tasks.
Generative Adversarial Networks (GANs) consist of two neural networks that compete to produce realistic data outputs.
Hallucination refers to AI models generating incorrect information, which poses risks for accuracy and reliability.
Inference is the process of running an AI model to make predictions based on previously learned data.
Large language models (LLMs) are deep neural networks that process language and generate responses based on vast amounts of text data.
Memory cache enhances inference efficiency by saving previous calculations for future queries, reducing computational demands.
Model Context Protocol (MCP) is an open standard that facilitates AI model connections to external tools and data.
Mixture of Experts (MoE) architecture allows neural networks to activate only relevant sub-networks for specific tasks, improving efficiency.
Neural networks are algorithmic structures inspired by the human brain, crucial for the development of deep learning and generative AI.
Open source AI models allow public access to underlying code for use and modification, exemplified by Meta’s Llama models.

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A Comprehensive Glossary of Key AI Terms for 2023

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