AI chatbot terms

A feature that allows models to consider larger amounts of information.

An AI powered system that captures lead information from potential customers around the clock, even outside of normal business hours.

An AI driven system that provides continuous support for ecommerce customers around the clock.

The structured semantic representation of a web page that assistive technologies and browser agents use to understand UI elements, roles, and state.

Information or insights that can be directly applied to solve problems, take actions, or inform decisions in AI systems.

Active learning is an ML technique where the model picks the most informative examples to label, cutting training data costs. See how it works in chatbots.

The practice of optimizing content to be cited or surfaced by AI answer engines like Google AI Overviews, Perplexity, and ChatGPT Search.

The ability of an agent or LLM to store, recall, and leverage past interactions or information during task execution.

A workflow structure in which AI agents autonomously plan, reason, and execute sequences of tasks using available tools and knowledge.

Advanced AI systems capable of performing tasks in the real world.

The simulation of human intelligence processes by machines, especially computer systems.

A measure of how well a website is prepared for AI agents to discover, understand, and interact with its content.

Discover how AI agents go beyond chatbots to autonomously plan, reason, and execute multi-step tasks using tools and external systems.

An AI assistant is a software program that uses large language models to understand natural language, answer questions, and complete tasks. Learn how they work and where they fit.

AI powered chatbots designed to assist customers in online stores with product discovery, support, and transactions.

The use of AI technologies to improve the creation, delivery, and personalization of online learning content.

The use of artificial intelligence to enhance customer support for SaaS platforms by automating responses and improving efficiency.

An AI hallucination is a confident but factually wrong answer from an LLM. Learn why it happens, how to detect it, and how to reduce it in chatbots.

An AI powered chatbot that provides medical information, patient triage, and healthcare support.

Google Search's dedicated conversational AI experience, a separate surface where users can have a multi-turn conversation with Google's generative model.

Google Search's generative AI feature that produces a summarized answer at the top of the results page, with citations to source pages.

Learn essential AI safety practices for deploying chatbots responsibly, including content filtering, guardrails, and harm prevention strategies.

A website feature that allows visitors to instantly ask questions about the current page content and receive accurate context aware answers using AI.

A search platform that uses large language models to generate direct answers to user queries instead of returning a list of links.

The practice of optimizing websites to be discovered, understood, and recommended by AI-powered search engines and language models.

AI solutions that assist students with academic, emotional, and administrative support.

An AI driven system that provides personalized teaching and guidance to students across various subjects.

A set of rules or procedures for solving a problem or accomplishing a task.

A mechanism for controlling how frequently users or agents can make API requests to prevent abuse and ensure fairness.

Application Programming Interfaces that enable GPTs to connect with other services and data.

An API designed for building conversational AI models.

Learn how attention mechanisms help AI models understand context by focusing on relevant parts of input text, enabling better language understanding.

Language models enhanced with external knowledge sources, such as embeddings or vector databases, to improve performance in tasks like retrieval and question answering.

Using AI to manage and schedule appointments with clients automatically, reducing administrative work and preventing double bookings.

An AI system capable of making decisions, planning, and acting independently to achieve specific goals.

An encoder that independently generates embeddings for queries and documents for scalable retrieval.

The character or persona a chatbot exhibits during interactions, defined by its tone, style, and manner of communication.

The process of improving a chatbot's performance by feeding it data and refining its algorithms.

An AI agent that operates a real browser, using visual rendering and the DOM or accessibility tree, to complete tasks on websites on the user's behalf.

Google's emerging conversational experience that lets brands chat with customers directly on Search, powered by their Google Business Profile.

AI systems designed to identify and re-engage customers who leave items in their online shopping carts.

A prompting technique where LLMs generate multi-step reasoning, explanations, or solutions by thinking step by step.

Chat history is the stored record of past conversations between users and a chatbot. Learn how it powers context, personalisation, and analytics in AI assistants.

The process of integrating and making a chatbot live on a platform or channel for users to interact with.

The sequence and structure of interactions that a chatbot follows during a conversation.

A chatbot framework is the set of tools used to build, test, and deploy conversational AI. Compare Rasa, Botpress, LangChain, and no-code options.

Quantitative measures used to evaluate and optimize the performance, efficiency, and user satisfaction of chatbots.

A software or service that provides tools and infrastructure to design, develop, train, and deploy chatbots.

Complete guide to training AI chatbots. Learn about training data, fine-tuning, RAG, and how to make your chatbot an expert on your business.

A subscription level for users to access advanced GPT models.

AI systems that analyze customer behavior and usage data to predict the likelihood of churn in SaaS businesses.

Using AI to automate the process of collecting initial information and documents from new clients.

A system or tool that allows LLMs to write, execute, and debug code in real-time as part of reasoning or automation.

The technique of dynamically adding relevant context or data into LLM prompts or agent workflows to improve accuracy and relevance.

The context window is the maximum amount of text a language model can read in a single request. Learn how it works, current limits per model, and how to handle long conversations.

A generation technique where AI models create content based on the broader context of the input, including user history and prior interactions.

Embeddings that capture the meaning of words or phrases based on the surrounding context.

A search method where the AI uses the meaning and context of the current page to answer queries.

The process of adding page specific or context relevant data into the AI's prompt before generating an answer.

A retrieval technique that incorporates the broader context of a query, such as user history or dialogue flow.

Discover how AI chatbots use contextual understanding to maintain conversation flow, remember previous messages, and provide relevant responses.

An AI system's ability to constantly adapt and improve its performance by learning from new data over time.

A software program designed to simulate conversation with human users.

Conversational AI is the branch of AI that lets software hold natural-language conversations. Learn how it works, the core tech, and real-world use cases.

The process of crafting effective and natural dialogues for chatbots and virtual assistants.

Learn how conversational interfaces use natural language to let users interact with websites and applications through chat rather than clicks.

User interfaces that enable interaction with the user in a conversational manner.

A cross-encoder is a transformer model that scores a query and document together for relevance, powering the reranking step in modern RAG pipelines. Learn how it works.

Tailored versions of ChatGPT for specific tasks or knowledge areas.

Features allowing users to set preferences for how ChatGPT operates.

The use of AI and automation tools to streamline the process of guiding new customers through SaaS onboarding.

The latest version of OpenAI’s image generation model.

A type of machine learning that uses neural networks with many layers.

A retrieval method that uses dense vector embeddings, enabling semantic search and advanced contextual retrieval.

The component of a chatbot that handles the flow of conversation.

Learn how dialogue management systems control conversation flow in AI chatbots, handling context, state tracking, and user intents effectively.

The process where a chatbot seeks clarity on ambiguous user input to provide the most accurate response.

The process of organizing and storing documents in a structured format to enable efficient retrieval.

The process of retrieving relevant documents from a collection, often powered by vector embeddings and semantic search.

A measure of how similar two or more documents are based on their content or embeddings.

The process of ensuring embeddings from different models or datasets are compatible for comparison or integration.

Dense numerical representations of data, such as text or images, used in tasks like semantic search and retrieval.

A training approach where a system learns to map inputs directly to outputs, minimizing intermediate steps or feature engineering.

Specific pieces of information extracted from user input, like names, dates, and products.

Learn how entity extraction (NER) helps chatbots identify names, dates, locations, and other key information from user messages automatically.

Structured or unstructured data repositories used by AI systems to retrieve information and enhance responses.

A default response given by a chatbot when it cannot understand or process the user's input.

A mechanism that allows systems to learn from their actions by receiving feedback on their performance.

Few-shot learning lets AI models learn a new task from just a few labelled examples, usually by showing them in the prompt. Learn how it works and when to use it in chatbots.

A prompting method where models are shown a small number of examples to guide their outputs for new tasks.

Adjusting a pre-trained model to perform better on specific tasks or datasets.

The process of adapting retrieval models to specific tasks or datasets by training them on task-relevant examples.

AI’s ability to execute specific tasks or functions within a program.

An interface that allows LLMs or agents to trigger external functions, APIs, or tools programmatically during reasoning.

AI systems designed to create new content, such as text, images, or audio, based on learned patterns and input context.

A training phase where AI models learn to predict and generate text based on large-scale datasets.

A synonym for AEO that emphasizes optimizing content for generative AI search surfaces like AI Overviews, AI Mode, and ChatGPT Search.

A marketplace for sharing and discovering custom GPTs.

An advanced GPT-4 iteration with enhanced speed and performance.

Learn how grounding connects AI responses to verified data sources, reducing hallucinations and improving accuracy in your AI chatbot.

Policies, rules, or constraints that ensure AI models act safely, ethically, and within desired boundaries.

A training method where human preferences or corrections are used to align and improve AI model behavior.

Human-in-the-loop is the pattern of keeping humans involved in AI workflows to review, approve, or escalate. Learn how HITL improves chatbot accuracy, training, and trust.

A search method that combines dense and sparse retrieval techniques to improve accuracy and relevance.

In-context learning is the ability of a large language model to pick up a new task from examples in the prompt, without any weight updates. Learn how it works and how to use it.

The process of updating retrieval system indices to reflect changes in the underlying data.

An AI tool that provides potential customers with an immediate, automated price estimate for services based on their inputs.

Set of protocols and tools that allow different software applications to communicate and work together.

The process of determining the specific goal or purpose behind a user's input in a conversation with a chatbot.

The ability of a chatbot to understand and identify what a user wants to achieve.

A format enabling structured data handling by AI models.

A lightweight, standardized protocol for remote procedure calls (RPC) using JSON, commonly used in AI system integrations.

A knowledge base is the curated content an AI chatbot draws on to answer questions accurately. Learn what to include, how to structure it, and how it powers RAG.

Learn what knowledge cut-off means in AI, why ChatGPT and other LLMs have training data limits, and how to work around these limitations.

A technique where a smaller model learns from a larger, more complex model, retaining critical knowledge while reducing size.

Learn how knowledge graphs organize information as connected entities and relationships, enabling AI to reason and answer complex questions accurately.

Structured representations of information, linking entities and their relationships to facilitate efficient knowledge retrieval.

The process of incorporating external knowledge into AI models to improve performance and accuracy.

The process of extracting relevant information from a knowledge base or database, using methods like semantic search and embeddings.

A technique that enhances AI model outputs by integrating retrieved knowledge into the generation process.

A generative AI approach where outputs are grounded in external knowledge sources, such as documents or databases.

Understand what large language models are, how they power ChatGPT and other AI assistants, and why they are revolutionizing conversational AI.

AI systems that automatically assess and qualify incoming leads based on predefined criteria to identify high value prospects.

A real-time communication method between customers and support agents or sales representatives via a website or application.

A technique where the AI dynamically generates and updates embeddings of the current page content in real time for highly accurate responses.

The process of coordinating large language models (LLMs), tools, and workflows to accomplish complex tasks.

A standardized markdown file located at the root of a website, designed to provide large language models (LLMs) with concise, structured information about the site’s content.

A subset of AI that allows systems to learn and improve from experience without being explicitly programmed.

Servers implementing the Model Context Protocol (MCP) to enable advanced, tool-augmented, and multi-modal LLM interactions.

A software component that communicates with MCP (Model Context Protocol) servers to enable advanced AI orchestration and tool-augmented workflows.

A method where external memory modules or databases are integrated with AI systems to enhance their knowledge and context retention.

Software layers that offer services and capabilities between the chatbot platform and external systems or databases.

Understand how AI alignment ensures models behave safely and helpfully. Learn about RLHF, constitutional AI, and alignment techniques.

Understand model inference—the process of using trained AI models to generate predictions and responses for user queries.

The process by which machine learning models learn from data.

A conversational AI approach where retrieval systems provide relevant information for multi-turn interactions.

Discover how multimodal AI models process multiple types of data including text, images, audio, and video for richer understanding and interaction.

The ability of AI to understand and generate different data types like text and images.

Interactions involving multiple modes or channels of communication, such as voice, text, and visuals.

A branch of AI that deals with the interaction between computers and human language.

Learn how NLU enables AI to interpret human language, extract meaning, and understand intent for accurate chatbot responses.

A training technique where irrelevant data points are sampled to improve the performance of retrieval models.

Computing systems inspired by the structure and functioning of the human brain.

A retrieval method that uses deep learning models to generate embeddings and match queries with documents.

A systems challenge where every one of N tools must be integrated with every one of M models or agents, leading to exponential complexity.

A core pattern in agentic AI where an agent observes the environment, reasons, and acts repeatedly to accomplish tasks.

Omnichannel is a customer experience strategy that keeps every channel (web chat, email, phone) in sync. Learn how it differs from multi-channel.

A task where AI systems answer questions using information retrieved from a wide range of unstructured data sources.

An open-source personal AI assistant that takes actions on your behalf across email, calendar, messaging, and the web.

The ability for an AI system to understand and use the specific content of the current page when generating responses.

A method where AI processes a user's query with direct reference to the current page content.

A retrieval technique that identifies specific passages within documents to answer user queries effectively.

The use of AI and automation tools to provide continuous support and engagement for patients.

A technique where chatbots recognize specific patterns in user input to generate responses.

A specific piece of data or instruction sent by a chatbot or received by it to trigger a certain action.

A modular system allowing developers to extend AI agent or LLM functionality by adding third-party tools, APIs, or workflows.

Machine learning models that have been previously trained on large datasets and can be fine-tuned or adapted for specific tasks.

AI models that are pre-trained on large datasets to understand and generate human language effectively.

Options for users to manage their data and how it's used by AI models.

The practice of designing effective prompts to guide AI models in generating desired responses.

Prompt injection is a security attack where users manipulate an AI chatbot into ignoring its instructions. Learn how direct and indirect injections work, and proven defences.

A technique where a model expands a single user query into multiple concurrent sub-queries to retrieve broader context before generating an answer.

An AI application that provides accurate and context-aware answers to user queries based on a knowledge base or retrieved data.

A tokenization method optimized for retrieval-augmented generation to balance efficiency and accuracy.

A system or component that enables AI agents to perform multi-step reasoning, logic, and decision-making.

A type of machine learning where models learn to make decisions through trial and error.

The consistency of AI in generating the same results under the same conditions.

The process by which AI systems produce replies or actions in response to user input.

Understand how AI chatbots generate responses, from retrieval and context building to text generation and output formatting.

A system that combines retrieval and generation processes to enhance AI model outputs with relevant knowledge.

A framework that combines retrieval and generation to produce responses grounded in external knowledge, leveraging techniques like dense retrieval and knowledge retrieval.

A technique that combines results from multiple retrieval methods to improve relevance and accuracy.

The time it takes for a retrieval system to fetch relevant information in response to a query.

Understand retrieval-based AI models, how they differ from generative models, and why they power modern chatbots with accurate, fact-based responses.

A model component used to encode queries and documents into embeddings for retrieval tasks.

A framework combining retrieval and generation models to produce accurate, context-rich responses.

Measures to prevent misuse of GPTs and ensure ethical usage.

Pre-defined replies or messages that a chatbot uses in specific scenarios or for certain user inputs.

Semantic search uses embeddings to match on meaning rather than keywords, so users find relevant content even when words do not overlap. Learn how it works and where to apply it.

Sentiment analysis classifies text as positive, negative, or neutral using NLP. Learn how it works and how AI chatbots use it to route conversations.

A single interaction or series of interactions between a user and a chatbot during a specific timeframe.

A technique used in conversational AI to gather specific pieces of information from the user.

A retrieval method that uses traditional term-matching techniques, such as TF-IDF or BM25, to find relevant documents.

A technology that converts spoken language into written text.

A system prompt is the hidden instruction that defines an AI chatbot's persona, rules, and constraints. Learn what to include, how to write one, and common pitfalls.

An AI assistant that supports telemedicine by streamlining virtual consultations and patient interactions.

Understand how the temperature parameter controls AI response randomness. Learn the best temperature settings for chatbots, creative writing, and factual tasks.

Learn how text embeddings transform words and sentences into numerical vectors, enabling semantic search and AI-powered information retrieval.

Discover how AI text generation works, from autocomplete to full conversations. Learn how LLMs predict and generate coherent, contextual text.

A technology that converts written text into audible speech.

Technology that converts digital text into spoken voice output.

Learn how thresholds work in AI chatbots to control confidence levels, trigger actions, and improve response accuracy in your applications.

Vector representations of individual tokens, such as words or subwords, used in language models.

Learn what token limits mean for AI chatbots, how they affect conversation length, and strategies to work within context window constraints.

The ability of AI agents or models to utilize external tools, APIs, or systems as part of their reasoning or action process.

A technique or model that augments large language models with autonomous tool usage for enhanced reasoning and real-world actions.

A design approach for integrating autonomous tool use and decision-making within large language models.

Data used to teach and refine machine learning models.

Discover how transformer architecture revolutionized AI. Learn how attention mechanisms enable GPT, Claude, and other large language models.

A machine learning architecture used primarily in the field of natural language processing (NLP).

The Turing Test measures whether an AI can converse indistinguishably from a human. Learn how it works, its limits, and how modern chatbots compare.

An emerging protocol referenced by Google that lets AI agents execute transactions across merchant sites in a standardized way.

A new iteration of GPT-3.5 with expanded capabilities and improved efficiency.

A metaphor for universal, plug-and-play standards in AI—enabling effortless compatibility between models, tools, and platforms.

Any input or phrase that a user communicates to a chatbot during a conversation.

A vector database stores embeddings for fast similarity search, powering semantic search and RAG-based chatbots. Learn how it works and how to pick one.

A digital assistant that uses voice recognition to interpret and respond to user commands.

Automated messages sent from apps when something happens.

A web-based implementation of the Model Context Protocol that allows AI agents to discover and interact with websites programmatically.

A small software component that provides a specific functionality, often used to integrate chatbots into websites or apps.

Zero-shot learning lets AI models perform tasks they were never explicitly trained on, using only an instruction. Learn how it works and when it beats few-shot or fine-tuning.

Learn how zero-shot prompting enables AI models to perform tasks without any examples, and when to use it versus few-shot prompting.