AI Glossary

Agentic AI

Artificial intelligence systems designed to pursue complex goals over time without continuous human intervention, capable of planning and executing multi-step tasks.

Artificial Intelligence (AI)

The simulation of human intelligence processes by machines, especially computer systems, encompassing learning, reasoning, and self-correction.

Computer Vision

A field of AI that enables computers and systems to derive meaningful information from digital images, videos, and other visual inputs.

Fine-Tuning

The process of taking a pre-trained language model and training it further on a smaller, specific dataset to adapt it to perform particular tasks or understand niche domains.

Generative AI

A category of AI algorithms that generate new outputs based on the data they have been trained on, including text, images, audio, and code.

Large Language Model (LLM)

A type of artificial intelligence algorithm that uses deep learning techniques and massively large datasets to understand, summarize, generate, and predict new content.

Machine Learning (ML)

A subset of AI that focuses on the development of algorithms and statistical models that enable computer systems to improve their performance on a specific task through data.

Natural Language Processing (NLP)

A branch of AI that helps computers understand, interpret, and manipulate human language, bridging the gap between human communication and computer understanding.

Prompt Engineering

The practice of designing and refining input prompts to effectively communicate with and guide generative AI models to produce desired outputs.

Retrieval-Augmented Generation (RAG)

An AI framework that improves the quality of LLM-generated responses by grounding the model on external sources of knowledge to supplement its internal representation of information.

Semantic Search

A data searching technique in which a search query aims to not only find keywords, but to determine the intent and contextual meaning of the words a person is using for search.

Vector Database

A type of database that stores data as high-dimensional vectors, enabling fast similarity search and retrieval, which is essential for RAG and semantic search applications.

Zero-Shot Learning

A machine learning paradigm where a model can perform a task it was not explicitly trained to do, relying on its general understanding of language and concepts.

Few-Shot Learning

A technique where an AI model is given a small number of examples (shots) in its prompt to help it understand the desired format or logic before generating a response.

Embeddings

Numerical representations of text, images, or audio in a continuous vector space, allowing AI models to understand the semantic relationships between different pieces of data.

Voice Activity Detection (VAD)

A technique used in speech processing wherein the presence or absence of human speech is detected, crucial for reducing latency in AI voice agents.

Text-to-Speech (TTS)

The process of converting written text into spoken voice output. Modern AI TTS systems can produce highly realistic, human-like voices with emotional inflection.

Speech-to-Text (STT)

Also known as Automatic Speech Recognition (ASR), this technology converts spoken language into written text, enabling voice agents to understand human callers.

Context Window

The maximum amount of text (measured in tokens) that an AI model can process and remember at one time during a single interaction or generation step.

Tokenization

The process of breaking down text into smaller units called tokens (which can be words, subwords, or characters) that language models use to process and generate language.

Hallucination

A phenomenon where an AI model generates false, fabricated, or nonsensical information that is not grounded in its training data or provided context.

Generative Engine Optimization (GEO)

The practice of optimizing content so that it is easily discoverable, understood, and recommended by AI-driven search engines and Large Language Models.