Glossary of terms

AI glossary for business readers

Algorithm – a set of rules or instructions given to an AI system to help it solve problems or make decisions.
Artificial Intelligence (AI) – the field of computer science that builds systems capable of tasks that usually require human intelligence, such as understanding language, recognizing patterns, or making decisions.
Bias (in AI) – systematic error in AI decision-making, often resulting from biased training data, which can lead to unfair or inaccurate outcomes.
Chatbot – a software application that uses AI to simulate human conversation, often used in customer service or sales.
Computer Vision – a field of AI that enables machines to interpret and process visual information from the world, such as images or video.
Data Labeling – the process of tagging or annotating data (e.g., images, text) to help train AI models by teaching them what patterns to recognize.
Deep Learning – a subset of machine learning that uses neural networks with many layers to analyze complex data, such as audio, images, or text.
Generative AI – a type of AI that creates new content, such as text, images, or music, by learning from existing examples.
GPT (Generative Pre-trained Transformer) – a large language model developed by OpenAI that can generate human-like text, answer questions, or assist with tasks.
Inference – the process by which an AI system applies its trained model to new, unseen data to make predictions or decisions.
Large Language Model (LLM) – a powerful AI model trained on massive text datasets to understand and generate natural language.
Machine Learning (ML) – a branch of AI where systems learn from data rather than being explicitly programmed for every task.
Model Training – the process of teaching an AI model to make predictions or decisions by feeding it labeled data.
Natural Language Processing (NLP) – the area of AI focused on enabling machines to understand, interpret, and generate human language.
Neural Network – a system of algorithms modeled after the human brain that enables AI to recognize patterns and make decisions.
Prompt – an instruction or input given to a generative AI system (like GPT) to guide its response.
Reinforcement Learning – a type of machine learning where an AI learns by receiving rewards or penalties for the actions it takes.
Supervised Learning – a type of machine learning where the model is trained on labeled data, meaning the correct answers are already known.
Token (in NLP) – a unit of text, such as a word or subword, used in the processing of language by AI models.
Training Data – the dataset used to teach an AI model how to recognize patterns and make predictions.

VoIP SIP terms

Auto Attendant – a virtual receptionist that answers calls and routes them to the right department or person without human intervention.
Bandwidth – the amount of data that can be transmitted over an internet connection in a given time, affecting VoIP call quality.
BYOD (Bring Your Own Device) – a policy allowing employees to use their personal devices (e.g., smartphones) for business communications, often integrated with VoIP apps.
Call Forwarding – a feature that redirects incoming calls to another number, such as a mobile phone or colleague’s extension.
Call Recording – the process of saving voice conversations, often used for training, compliance, or customer service quality control.
Codec – a technology that compresses and decompresses voice data to optimize quality and bandwidth usage during VoIP calls (e.g., G.711, G.729).
DID (Direct Inward Dialing) – a phone number that rings directly to a specific VoIP extension without going through a menu or operator.
DTMF (Dual-Tone Multi-Frequency) – the tones generated when you press telephone keys, used to interact with automated systems.
Endpoint – any device that connects to a VoIP system, such as an IP phone, softphone, or VoIP-enabled mobile app.
Hosted PBX – a cloud-based phone system where the service provider hosts and manages all call routing and features.
IP Phone – a physical telephone designed to work over the internet using VoIP protocols instead of traditional phone lines.
IVR (Interactive Voice Response) – an automated phone system that allows callers to interact with a menu using voice or keypad input.
Jitter – variability in the arrival time of voice packets, which can cause poor audio quality in VoIP calls.
Latency – the delay between speaking and the other person hearing your voice, which can impact the natural flow of conversation.
Packet Loss – when some voice data packets fail to reach their destination, leading to choppy or missing audio.
PBX (Private Branch Exchange) – a private phone network used within a company that manages internal and external calls.
PoE (Power over Ethernet) – technology that allows IP phones to receive both power and internet connection through a single Ethernet cable.
SIP (Session Initiation Protocol) – a standard protocol used to start, manage, and end VoIP calls over the internet.
Softphone – software that turns a computer or mobile device into a phone using VoIP technology.
VoIP (Voice over Internet Protocol) – a technology that enables voice calls to be made over the internet instead of traditional phone lines.

LangChain and AI agents glossary for business readers

Agent (AI Agent) – a component of an AI system that can make decisions, use tools, and carry out multi-step tasks based on a goal, often acting autonomously.
Chain – a sequence of steps or calls that an AI model follows to process input and deliver a result; for example, first extract data, then analyze it.
Context Window – the amount of information (tokens) an AI model can “remember” or process at once; crucial for long conversations or documents.
Embeddings – numerical representations of text or data used to help the AI understand meaning and find similar items (e.g., for search or categorization).
Function Calling (Tool Use) – the ability of a language model to call external tools or APIs during its reasoning, such as searching a database or checking a calendar.
LangChain – an open-source framework that connects language models with tools, APIs, and data sources, allowing developers to build complex, goal-driven AI apps.
LLM (Large Language Model) – the core engine behind many LangChain workflows; a powerful model like GPT-4 that understands and generates human-like text.
Memory (in agents) – a system that allows AI agents to remember past interactions or facts, enabling more personalized or context-aware responses.
Multi-modal Agent – an AI agent that can work with multiple types of data, like text, images, or voice, to perform tasks or answer questions.
Prompt Template – a structured way of building dynamic prompts with variables (e.g., "Summarize this: {text}"), which makes AI outputs more predictable and reusable.
Retriever – a component that searches for the most relevant documents or data from a larger knowledge base, often used before AI generates an answer.
ReAct Pattern (Reasoning + Acting) – an approach where an AI agent reasons step-by-step, chooses a tool to act, and then continues based on the result — useful in complex tasks.
RAG (Retrieval-Augmented Generation) – a method where the AI first retrieves relevant documents, then uses them to generate a more accurate and informed response.
Tool – an external function or service (like a calculator, web search, or CRM) that an AI agent can use to complete tasks.
Tool Agent – a type of agent specifically designed to choose and use tools (like APIs or functions) to answer questions or perform operations.
Vector Store – a database that stores embeddings, allowing the AI to search for and retrieve information based on meaning, not just keywords.