AI Terms to Know

Large Language Model (LLM)

A type of artificial intelligence trained on massive amounts of text data to understand, generate, and respond to human language.

Generative AI (Gen AI)

A type of AI that can create new, original content like text, images, audio, and video by learning patterns from existing data.

Prompt

The instruction, question, or input provided by a user to guide the AI's response.

Token

The fundamental unit of data that a model processes, which can be a word, part of a word, or a character. LLMs break down text into these tokens to understand and generate human language, with each unique token being assigned a specific numerical ID.

Context Window

The maximum amount of text (measured in tokens) that an AI model can process in a single interaction. This includes both the input prompt and the generated response. Larger context windows allow models to handle longer documents and maintain coherent conversations over more exchanges.

Foundation Model

A large-scale AI model trained on broad, diverse data that can be adapted for various downstream tasks. Foundation models (like GPT-4, Claude, or Gemini) serve as the base for specialized applications through fine-tuning or prompting, rather than being built for a single specific purpose.

Pre-training

The initial phase of training a large language model on massive amounts of unlabeled data from diverse sources (websites, books, articles) to learn general language patterns, facts, and reasoning capabilities. This foundational training occurs before any task-specific fine-tuning.

Semantic Understanding

The ability to grasp meaning and relationships between concepts beyond literal text matching. In AI contexts, semantic understanding allows models to comprehend intent, context, and connections between ideas. Semantic HTML and structured data help both search engines and AI models better interpret content meaning.

Generative (or Answer) Engine Optimization (GEO or AEO)

A marketing-based term meant to apply SEO (Search Engine Optimization) like practices to digital content so that AI-powered search tools can more easily cite, summarize, and synthesize it into direct answers.

AI Agent

Autonomous system that perceives, reasons, acts, and observes to achieve goals.

Hallucination

When an AI model generates incorrect or fabricated information that is not based on its training data.

Chain-of-Thought (Reasoning)

The step-by-step logical process where AI models break down complex problems to arrive at conclusions. Advanced prompting techniques like Chain-of-Thought, Tree-of-Thought, and Graph-of-Thoughts explicitly structure this reasoning process, improving model performance on analytical tasks by 2-3× compared to direct answers.

Fine-Tuning

The process of adapting a pre-trained model to perform a more specific task or domain.

Reinforcement Learning from Human Feedback (RLHF)

A training method to align an AI with human preferences.

Model Context Protocol (MCP)

Agentic Commerce Protocol (ACP)

Agent2Agent (A2A)

Vector Database

Stores data as numerical vectors, enabling semantic similarity searches.

Retrieval Augmented Generation (RAG)

Enhances LLM prompts by retrieving relevant context from a vector database.