This glossary provides definitions for key terms related to conversational AI, including conversational AI, AI bots, chatbots, RAG, AI, machine learning, deep learning, GPT, LLM, transformer models, intent recognition, entity recognition, dialogue management, and sentiment analysis and more.

Conversational AI

A branch of artificial intelligence that enables computers to understand, process, and respond to human language in a natural and conversational manner. This technology underpins various applications, including virtual assistants, customer service bots, and conversational agents.

AI Bot

Short for artificial intelligence robot, these are software applications that perform automated tasks. In the context of conversational AI, these bots simulate human conversation through text chats or voice commands.


A software application used to conduct an online chat conversation via text or text-to-speech, in lieu of providing direct contact with a live human agent. Designed to convincingly simulate the way a human would behave as a conversational partner.


An AI system that uses a pre-trained algorithm to make predictions or inferences. These models are typically trained on large datasets and can be used for a variety of tasks, including language translation, image recognition, and text generation.


RAG stands for Retrieval-Augmented Generation. It is a technique that enhances the response generation process by retrieving information from a large database or knowledge base, then using this information to inform the generation of a response. This approach helps in providing more accurate, relevant, and informative answers.


AI stands for Artificial Intelligence. It is the simulation of human intelligence processes by machines, especially computer systems. This includes learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions), and self-correction.


ML stands for Machine Learning. It is a subset of the field of AI that involves training algorithms to learn from and make predictions or decisions based on data, without being explicitly programmed for the task. This is fundamental in training AI models, including those used in conversational AI.

Deep Learning

A subset of machine learning that uses neural networks with many layers (deep neural networks) to learn from large amounts of data. Deep learning is crucial for processing complex inputs such as images, sound, and text.


GPT stands for Generative Pre-trained Transformer. It is a type of artificial intelligence model used for understanding and generating human-like text. GPT models are trained on vast amounts of text data, enabling them to generate coherent and contextually relevant text based on input prompts.


LLM standards for Large Language Model. It is a type of AI model that processes and generates human-like text. These models are trained on extensive text data, allowing them to produce lengthy and contextually relevant text passages, aiding in tasks like text completion, translation, summarization, and more.

Transformer Models

A type of model architecture used in deep learning that has been highly effective in various NLP tasks. It relies on mechanisms called attention to weigh the influence of different parts of the input data differently.

Intent Recognition

The process of identifying the intention behind a user's input in conversational systems. This allows the AI to understand what the user wants to achieve and respond appropriately.

Entity Recognition

Also known as Named Entity Recognition (NER), this is the task of identifying and classifying key information (entities) in text into predefined categories such as the names of persons, organizations, locations, expressions of times, quantities, monetary values, percentages, etc.

Dialogue Management

The component of conversational AI systems responsible for managing the flow of a conversation. It determines the next best response based on the context of the conversation and the system's objectives.

Sentiment Analysis

The process of computationally determining whether a piece of writing is positive, negative, or neutral. It's often used in conversational AI to understand the emotional tone of user inputs.

No Code

A term used to describe the development of applications, websites, and other digital products without needing to write code. This approach leverages visual programming interfaces, allowing non-technical users to create functional software.

Low Code

A term used to describe the development of applications, websites, and other digital products with minimal coding effort. It utilizes some visual programming interfaces and pre-built components, allowing developers to build software faster and with less manual coding.


BM25 is a ranking function used by search engines to rank matching documents according to their relevance to a given search query. It's based on the probabilistic retrieval framework and incorporates term frequency (TF) and inverse document frequency (IDF) to calculate relevance.

A technique used to find the most similar items to a query in a high-dimensional vector space. It's commonly used in information retrieval and recommendation systems, leveraging the mathematical properties of vectors to find similarities. Recently this technique is used for building RAG systems!