AI Glossary
Every term you'll encounter building, deploying, and scaling AI agents, defined clearly.
Agent
A software system that perceives its environment, makes decisions, and takes actions autonomously to achieve a goal.
Related: AI Agent, Autonomous Agent
Agentic AI
AI systems designed to act autonomously with goal-directed behavior, planning, tool use, and iterative reasoning.
Related: AI Agent, Orchestration
AI Agent
An agent powered by artificial intelligence that can reason, plan, and execute tasks using language models and external tools.
Related: Agentic AI, Agent
API (Application Programming Interface)
A set of protocols and definitions that allow software components to communicate with each other programmatically.
Related: Integration Layer
Autonomous Agent
An agent that operates independently with minimal human oversight, making decisions and completing tasks end-to-end.
Related: Agent, Human-in-the-Loop
Baseline Model
A reference model used as a starting point for comparison when evaluating improvements from fine-tuning or new architectures.
Related: Foundation Model, Fine-Tuning
Batch Processing
Running AI inference or training tasks on collected groups of inputs rather than individually in real time.
Related: Inference, Workflow
Bot
A software application that automates repetitive tasks, often through predefined rules or AI-driven conversation.
Related: Chatbot, AI Agent
Chatbot
A conversational interface that interacts with users via text, often powered by NLP or large language models.
Related: Dialog Management, NLP
Confidence Score
A numerical value indicating how certain a model is about its prediction or output, typically between 0 and 1.
Related: Output Validation, Inference
Context Window
The maximum number of tokens a language model can process in a single interaction, including both input and output.
Related: Token, Tokenization
Conversation AI
AI systems designed to engage in natural, multi-turn dialogue with users, handling context and follow-up questions.
Related: Dialog Management, Multi-Turn Conversation
Deep Learning
A subset of machine learning using multi-layered neural networks to learn hierarchical representations from raw data.
Related: Neural Network, Transformer
Decision Tree
A supervised learning model that splits data into branches based on feature values to make predictions.
Related: Model, Training Data
Dialog Management
The component of a conversational system that tracks dialogue state and determines the next action or response.
Related: Intent Recognition, Multi-Turn Conversation
Embedding
A dense vector representation of text, images, or data in a continuous vector space, capturing semantic meaning.
Related: Vector Database, RAG
End-to-End Automation
Automating an entire workflow from initial input to final output with minimal or no human intervention.
Related: Workflow, Workforce Automation
Entity Recognition
Identifying and classifying named entities (people, organizations, locations) within unstructured text.
Related: NLP, Intent Recognition
ETL (Extract, Transform, Load)
A data pipeline process that extracts data from sources, transforms it, and loads it into a target system.
Related: Workflow, Unstructured Data
Fine-Tuning
Adapting a pre-trained model to a specific task or domain by continuing training on a smaller, targeted dataset.
Related: Foundation Model, Training Data
Foundation Model
A large-scale model pre-trained on broad data that can be adapted to a wide range of downstream tasks.
Related: Large Language Model (LLM), Fine-Tuning
Few-Shot Learning
Training or prompting a model with only a small number of labeled examples to generalize to a new task.
Related: Zero-Shot Learning, Prompt Engineering
Governance
Policies, processes, and oversight frameworks that ensure AI systems are used responsibly and compliantly.
Related: Guardrails, RBAC
Grounding
Anchoring AI outputs to verified, real-world data sources to reduce hallucinations and improve factual accuracy.
Related: RAG, Hallucination
Guardrails
Safety mechanisms and constraints that prevent AI systems from producing harmful, biased, or off-topic outputs.
Related: Governance, Output Validation
Hallucination
When a model generates plausible-sounding but factually incorrect or fabricated information.
Related: Grounding, Confidence Score
Harness
An operational framework that wraps around an AI model to manage inputs, outputs, tool calls, and workflow orchestration.
Related: Workflow, Orchestration
Human-in-the-Loop (HITL)
A design pattern where a human reviews, validates, or approves AI decisions at critical points in a workflow.
Related: Governance, Output Validation
Inference
The process of using a trained model to generate predictions or outputs from new input data.
Related: Model, Latency
Intent Recognition
Identifying the user's goal or purpose from their input text to drive the appropriate system response.
Related: NLP, Entity Recognition
Integration Layer
Middleware that connects AI systems to external APIs, databases, and enterprise tools for data exchange.
Related: API, Workflow
Knowledge Base
A structured repository of information that AI systems query to provide accurate, domain-specific responses.
Related: Knowledge Graph, RAG
Knowledge Graph
A network of entities and their relationships, enabling AI to reason over structured domain knowledge.
Related: Knowledge Base, Embedding
Large Language Model (LLM)
A neural network with billions of parameters trained on massive text corpora to understand and generate human language.
Related: Foundation Model, Transformer
Latency
The time delay between sending a request to an AI model and receiving the response.
Related: Inference, Token
Learning Rate
A hyperparameter controlling how much model weights are updated during each training step.
Related: Training Data, Fine-Tuning
Model
A mathematical representation trained on data to perform tasks like prediction, classification, or generation.
Related: Foundation Model, Training Data
Multi-Turn Conversation
A dialogue exchange spanning multiple back-and-forth interactions, requiring the system to maintain context.
Related: Context Window, Dialog Management
Memory
The ability of an AI agent to retain and recall information across interactions within or across sessions.
Related: Context Window, Multi-Turn Conversation
NLP (Natural Language Processing)
A field of AI focused on enabling computers to understand, interpret, and generate human language.
Related: Intent Recognition, Sentiment Analysis
Neural Network
A computing architecture inspired by biological neurons, composed of layers of interconnected nodes that learn patterns from data.
Related: Deep Learning, Transformer
Orchestration
Coordinating multiple AI agents, tools, and workflows into a unified, sequential or parallel process.
Related: Workflow, Integration Layer
Output Validation
Checking AI-generated outputs for correctness, safety, format compliance, and adherence to business rules before delivery.
Related: Guardrails, Confidence Score
Prompt
The input text or instruction given to a language model to guide its generation or behavior.
Related: Prompt Engineering
Prompt Engineering
The practice of designing and refining prompts to elicit desired outputs from language models.
Related: Prompt, Few-Shot Learning
PII (Personally Identifiable Information)
Any data that can identify a specific individual, requiring strict handling and redaction in AI systems.
Related: Governance, Guardrails
Query
A request sent to a model or database to retrieve or generate specific information.
Related: Prompt, RAG
Quality Score
A metric evaluating the accuracy, relevance, and reliability of an AI system's outputs.
Related: Confidence Score, Output Validation
RAG (Retrieval-Augmented Generation)
A technique that combines a language model with external knowledge retrieval to ground responses in verified data.
Related: Knowledge Base, Grounding
RBAC (Role-Based Access Control)
A security model that restricts system access based on user roles and permissions.
Related: Governance
Reinforcement Learning
A training paradigm where an agent learns optimal behavior by receiving rewards or penalties for its actions.
Related: Agent, Fine-Tuning
Sentiment Analysis
Using NLP to determine the emotional tone (positive, negative, neutral) of text input.
Related: NLP, Intent Recognition
Simulation
Running AI agents in a controlled virtual environment to test behavior before real-world deployment.
Related: Agent, Validation
Step
A single discrete action within a workflow, such as an API call, data transform, or model inference.
Related: Workflow, Orchestration
Structured Output
AI responses returned in a predefined schema (JSON, XML) for easy parsing and downstream processing.
Related: Output Validation, Query
Token
The smallest unit of text processed by a language model, which may be a word, subword, or character.
Related: Tokenization, Context Window
Tokenization
The process of breaking text into tokens that a language model can process.
Related: Token, NLP
Training Data
The dataset used to teach a model patterns and relationships during the training process.
Related: Model, Fine-Tuning
Transformer
A neural network architecture using self-attention mechanisms that powers most modern language models.
Related: Large Language Model (LLM), Deep Learning
Unstructured Data
Data without a predefined format, such as emails, documents, images, or audio files.
Related: Embedding, ETL
Vector Database
A specialized database optimized for storing and querying high-dimensional vector embeddings for similarity search.
Related: Embedding, RAG
Validation
Testing an AI model's performance on unseen data to assess generalization and detect overfitting.
Related: Output Validation, Quality Score
Workflow
A defined sequence of steps and decisions that an AI agent or system follows to complete a task.
Related: Orchestration, Step
Workforce Automation
Replacing or augmenting repetitive human tasks with AI agents to improve speed, consistency, and scale.
Related: Agent, End-to-End Automation
Zero-Shot Learning
A model's ability to perform a task it was never explicitly trained on, using only its general knowledge.
Related: Few-Shot Learning, Foundation Model