Meera Conversational AI Glossary

AI Chatbots are software applications that use AI to provide human-like responses to user inquiries. These can include self-service for accounts, general questions about products and services, and other company-specific requests. Chatbots continue to grow in popularity as businesses look for better ways to connect with their customers, especially outside of normal business hours. Chatbots are always available and ready to help customers address any potential issues they may have, making them a great tool for sales and customer service teams.

Application Programming Interfaces (APIs) involve the integration of a chatbot with an existing system or service. APIs allow chatbots to push and pull data, retrieve information, and carry out actions requested by a user. With modern software as a service solutions, APIs remain the standardized method for connecting the flow of data between two apps. Almost all devices or platform connected to the Internet of Things leverage APIs to function.

AHT refers to the average duration taken by a customer service representative to handle a customer interaction from start to finish. It encompasses the time spent actively engaging with a customer, resolving issues, and any associated follow-up tasks. Monitoring AHT helps organizations optimize their operational efficiency and enhance customer service by streamlining processes.

Chatbot platforms are any software or platform that provides the infrastructure and tools needed to build, deploy, and manage chatbots. Chatbot platforms typically leverage natural language processing, dialog management, TTS and SST, and integrations with popular messaging and communication channels. These platforms allow any business to develop a specialized chatbot that is capable of navigating through industry-specific interactions with a user. Additionally, chatbot platforms may leverage APIs to integrate other features a business may need.

A chatbot script is a predefined sequence of instructions or responses programmed into a chatbot system. These scripts guide the chatbot's conversational flow, providing specific replies or actions based on user queries or inputs. They play a pivotal role in ensuring consistent and accurate interactions between the chatbot and users across various scenarios and inquiries.

Contextual understanding covers a chatbot’s ability to recall relevant contextual information from earlier parts of a conversation. This allows chatbots to provide relevant responses that are coherent and personalized to a specific user’s needs. For example, a chatbot may recall that a user mentioned they are gluten intolerant when ordering food. Knowing this, the chatbot wouldn't recommend any menu items that may contain gluten as it posesses the necessary contextual understanding.

Continuous learning is a chatbot’s capability to improve performance over time based on feedback and interactions with users. Continuous learning typically includes model updates, refining responses, adapting to user needs, and ensuring the chatbot is capable of providing the most up to date and relevant information. This method of learning plays an important role in ensuring a chatbot is capable of evolving over time as a specific needs of an industry, company, or its customers shift.

Conversational AI is one type of AI focused on simulating human-like conversations between computers and humans. Natural Language Processing (NLP) and machine learning are two of the main foundational models that have made conversational AI possible. Conversational AI is largely tied to generative AI. Training a conversational AI requires an immense amount of data to ensure it is capable of interacting with humans using natural responses. Common versions of conversational AI include generative AI agents, chatbots, and virtual assistants.

Conversation design is the process of designing the potential flow of a conversation with a chatbot. This type of design aims to define and predict how conversations happen to ensure the best possible user experience. It works by combining technology, psychology, and language to create human-like experiences. Conversational design includes building conversations, mapping out user flows, creating effective prompts, and using them to provide quality responses that closely mimic human conversation.

Dialog flow refers to the sequence of interactions between a user and an AI system, often in a conversational manner. It encompasses the back-and-forth exchanges where the AI processes user inputs, generates appropriate responses, and maintains context throughout the conversation. A well-designed dialog flow enhances user experience by enabling natural and coherent interactions with AI systems.

Dialog management is the process of managing a conversation’s flow and context between a chatbot and user. Dialog management ensures a chatbot responds correctly and logically to user inputs. For example, if a user is trying to order a coffee, a chatbot will ask the user what size they want. However, if the user asks to change the coffee into a breakfast sandwhich, the chatbot will need to revert to the appropriate dialog flow for that new item.

Entity extraction covers the process of identifying and extracting relevant pieces of information from user inputs. This process works by extracting specific details from text and classifying them into categories. For example, a flight booking chatbot may look for any mentions of dates, preferred airlines, or other important details by a user to provide the most relevant response. This is an important process as it allows AI to identify patterns and navigate conversations with enhanced accuracy.

Entity recognition involves identifying and extracting specific pieces of information, such as names, dates, locations, or other entities, from unstructured data like text. It enables AI systems to comprehend and process crucial details within user inputs, contributing to more accurate and context-aware responses.

Fall back intent serves as a fail-safe mechanism within an AI system. When the system is unable to determine a user's intent accurately, it triggers predefined alternative actions or responses to handle the situation gracefully, ensuring a better user experience despite the ambiguity.

A Frequently Asked Question (FAQ) bot is a specialized type of chatbot for providing answers to frequently asked questions. FAQ bots use a combination of natural language processing (NLP) and a rules-based approach to provide predefined responses to common user questions. When used correctly, FAQ bots provide companies with a powerful opportunity to create a more engaging way to provide answers to popular questions about a companies products and services.

FCR signifies resolving a customer's query, issue, or request during their initial interaction with a customer service representative or system. Achieving FCR reduces customer effort and enhances satisfaction by efficiently addressing concerns without necessitating multiple contacts or escalations.

Hyperautomation involves the comprehensive integration of various automation technologies, including AI, machine learning, robotic process automation (RPA), and other tools. It aims to automate and optimize end-to-end business processes across diverse domains, significantly enhancing efficiency and productivity.

Intent recognition is the process used to determine a user’s intent in conversations with an AI. This process leverages both machine learning and natural language processing (NLP). Chatbots rely on intent recognition to identify what a user is looking to do and provide relevant responses that solve intent. Intent plays an important role in conversational AI as it reduces the likliehood of a chatbot providing an incorrect or inappropriate answers based on the user's original intent.

In natural language understanding, an intent slot acts as a placeholder or variable within an intent that captures specific pieces of information from user inputs. Slots help in extracting relevant details like dates, names, or quantities, aiding AI systems in better understanding user intentions and providing accurate responses accordingly.

Machine learning is a subfield of AI that involves training computer systems with data to allow them to better understand, predict, and respond to human behavior. Machine learning is a core part of chatbot technology and often goes hand in hand with natural language processing (NLP). At the most basic level, machine learning uses data and algorithms to simulate human learning. There are four types of machine learning, these include supervised learning, unsupervised learning, semisupervised learning, and reinforcement learning.

A machine learning model is an algorithmic system designed to learn patterns and make predictions or decisions based on input data. These models use statistical techniques to improve their performance over time and are widely applied across diverse domains, ranging from image recognition to recommendation systems.

Multi-turn conversations are any conversation that involves multiple exchanges between a chatbot and user. Chatbots use multi-turn conversations to maintain contextual relevance by ensuring responses account for relevant details from current and past interactions. Multi-turn conversations are important because they allow for more complex interactions beyond simple requests. For example, a user looking to text a friend may request to send a text as the first turn in the conversation while the second turn would include them telling the AI what they want the message to say.

Multimodal AI refers to AI systems capable of processing and understanding multiple forms of input, such as text, images, speech, and other data modalities. These systems integrate information from various sources to generate more comprehensive and contextually rich responses or actions.

NLG involves the process where AI systems generate human-like text or narratives based on structured data or prompts. These systems transform data into coherent and contextually appropriate language, enabling the automatic creation of reports, summaries, or other textual content.

Natural Language Processing (NLP) combines computer science and linguistics to allow computer systems to understand human language. This technology is what allows chatbots to understand and respond to user inquiries. NLP has two distinct phases, including data preprocessing and algorithm development. Data preprocessing works to clean the text for a machine to analyze using either a rules-based system or a machine learning-based system. Additionally, syntax and semantic analysis are the two most common methods of natural language processing.

NLU refers to an AI's ability to comprehend and interpret human language in a manner that captures context, meaning, and intent. NLU enables systems to process and derive meaning from spoken or written language, facilitating accurate responses and interactions.

A persona represents a fictional but detailed character profile created to embody and represent a specific target audience or user group. These personas aid in understanding user behaviors, preferences, and needs, guiding the development of products or services tailored to those demographics.

Sentiment analysis uses both NLP and machine learning to identify the intent and emotion behind a conversation. Sentiment analysis works by classifying the tone of the message as positive, negative, or neutral. This is incredibly useful for companies with a large amount of customer data like emails and reviews they wish to analyze. It's also valuable for chatbots as it allows them to better understand and provide human-like responses based on a user’s perceived emotional state.

Speech recognition is a form of technology that allows applications to understand and convert verbal conversations into written text. This technology is built on the foundation of natural language processing (NLP). Speech recognition enables users to communicate with chatbots using voice commands. This is important as it ensures an AI solution can effectively understand verbal language. Virtual assistants like Siri, Alexa, and Google Assistant are just a few of the popular examples of speech recognition platforms in the market.

Speech-to-text (STT) technology turns verbal conversations into written text. Virtual assistants use STT to transcribe and understand a user’s verbal inputs. It's important to understand that speech-to-text does not refer to text captions, but instead to process required to create them. Speech-to-text plays an important role in the user experience as humans often like to communicate their needs verbally. For example, this could include asking an AI assistant to set a timer, send a text, or tell them about the weather.

Text blasting involves sending a large volume of messages or texts simultaneously to a group of recipients. This mass communication approach is often used for announcements, promotions, or marketing campaigns.

Text-to-speech (TTS) technology turns written text in verbal conversation. AI solutions like virtual assistants use TTS to verbally respond to users, creating a more human-like experience. While text-to-speech started as an assistive technology, it has since evolved and serves as an important part of AI platforms. For example, text-to-speech solutions are being used for sales, customer service, and any form of customer engagement that may require verbal communication.

Training data refers to the set of labeled or annotated information used to train machine learning models. It forms the basis for the model's learning process, helping it recognize patterns and make accurate predictions or classifications.

The Turing Test assesses an AI's ability to exhibit behavior indistinguishable from that of a human during conversations. If an AI can convincingly simulate human-like responses, it is considered to have passed the Turing Test.

User personas act as a fictional profile of a target user group. There are four distinct personas to consider, these include goal-directed personas, role-based personas, engaging personas, and fictional personas. User personas allow AI solutions like chatbots to understand user needs, preferences, and behaviors so they can provide human-like responses that are personalized and address user intent. They also play an important role in prompt engineering by allowing a user to define specific characteristics for a persona before asking for an answer.

An utterance refers to any spoken or written expression, statement, or input made by a user to interact with an AI system. It encompasses queries, commands, or any form of communication directed towards the AI.

A virtual agent is an AI-powered software program designed to simulate human-like interactions with users. These agents assist users by providing information, answering queries, or performing tasks within predefined boundaries.

A virtual assistant is an AI-powered chatbot designed to help user’s with a variety of tasks. These tasks can include responding to questions, making recommendations, booking meetings, finding files, and more. Some of the most popular AI virtual assistants include Siri, Alexa, and Google Assistant. Virtual assistants are built on the foundations of natural language processing (NLP) and machine learning. Additionally, they intersect with other popular technologies like conversational AI, speech-to-text, and text-to-speech to create a positive user experience.

A voice assistant is an AI-driven application or device that responds to voice commands or queries provided by users. These assistants perform various tasks, including setting reminders, retrieving information, controlling smart devices, and more, based on voice inputs.

Live agent handoff refers to the process where a conversation initiated with an automated system, such as a chatbot or virtual agent, is transferred to a human agent for further assistance. This transfer typically occurs when the automated system reaches its limitations or when the user's query requires personalized or complex attention beyond the AI's capabilities.

LLM stands for Language Model, which encompasses AI-based models trained on vast amounts of text data to understand and generate human-like language. These models, such as GPT (Generative Pre-trained Transformer), facilitate various natural language processing tasks, including text completion, translation, summarization, and more.