AI Glossary: Key Terms in Generative AI, ChatGPT, and Machine Learning

This glossary defines the essential terms businesses need to understand when adopting generative AI tools responsibly.

AI Beta Team – A select group within an organization tasked with testing Generative AI tools safely, documenting best practices, and guiding rollout.
AI Hallucination Mitigation – A practice of validating AI outputs through fact-checking, cross-model comparison, or linking with RAG systems.
AI Usage Policy – A firm’s internal rules governing how Generative AI can be used responsibly, covering risks like confidentiality, hallucinations, and bias.
Anthropic – AI company known for its Claude conversational model.
Answer Engine Optimization (AEO) – A content strategy shift: optimizing for AI-powered search engines and chatbots instead of traditional SEO.
Artificial Intelligence (AI) – Computer systems designed to perform tasks that usually require human intelligence, like reasoning, learning, or problem-solving.
Bias – Systematic errors in training data that cause an AI model to produce unfair or skewed results.
ChatGPT – An LLM created by OpenAI designed to generate conversational text responses.
ChatGPHD – A knowledge-level system you created, modeled like martial arts belts or challenge coins, to measure AI proficiency.
Claude – A conversational LLM developed by Anthropic, similar to ChatGPT.
Closed-Loop Generative AI – A system where AI runs in a secure, controlled environment without external data exposure (key for law firms and healthcare).
Closed-Loop System – A setup where AI operates in a controlled environment, limiting external risks.
Confidentiality Risk – The danger of exposing private or client data when entered into public LLMs.
Deep Learning – A branch of Machine Learning that uses multi-layered Neural Networks to model complex patterns in data.
Deep Research – A process of using AI models (sometimes across multiple LLMs) to cross-check and reduce hallucinations.
Diffusion Model – A Generative AI model (used in image tools like Stable Diffusion) that creates images by gradually transforming noise into a picture.
Embedding – A way of representing words, sentences, or images as numerical vectors so AI models can understand similarity.
Ethical AI – The practice of building and using AI responsibly, addressing issues like bias, privacy, and safety.
Few-Shot Learning – When a model is given a handful of examples in the prompt to guide its response.
Fine-Tuning – Adjusting a pre-trained LLM on a smaller, specialized dataset for a specific task.
Fractional AI Leadership – Outsourced executive leadership (like a Fractional COO) focused on bringing AI adoption into organizations safely and strategically.
GAN (Generative Adversarial Network) – A Deep Learning model where two Neural Networks (generator and discriminator) compete to create realistic content.
Gemini – Google’s LLM, a successor to Bard, designed for multimodal AI tasks.
Generative AI – A type of Artificial Intelligence that creates new content such as text, images, audio, or video.
Generative AI Accelerator – A structured workshop you created to move teams from basic awareness to hands-on AI usage with accountability.
Generative AI for Law Firms – Specialized AI adoption focusing on compliance, data privacy, and intellectual property risks.
Generative Engine Friendly – Your phrase for websites optimized not just for humans or Google, but for LLMs like ChatGPT, Claude, and Gemini.
Generative Engine Optimization (GEO) – Emerging practice of optimizing content for AI-powered search and answer engines instead of just traditional search engines.
Generative AI Mastermind – A peer group session where small business owners or professionals learn, share, and apply AI use cases.
Grok (xAI) – An LLM from Elon Musk’s xAI, integrated with X (Twitter).
Hallucination – When a Generative AI confidently produces false or made-up information.
Hallucination Risk – The tendency of LLMs to generate inaccurate or fabricated information presented as fact.
Large Language Model (LLM) – A Deep Learning model trained on massive amounts of text to understand and generate human-like language.
Machine Learning (ML) – A subset of AI where systems learn patterns from data to make predictions or decisions without explicit programming.
Meta AI (LLaMA) – Meta’s family of LLMs used for research and open-source projects.
Model Parameters – The internal values of a Neural Network that adjust during training to learn patterns.
Multimodal AI – An AI system that works across multiple types of data, such as text, images, and audio.
MyGPT – A customized version of ChatGPT that can be tailored with specific instructions, documents, or workflows for personal or organizational use.
Neural Network – Algorithms inspired by the human brain that process data through interconnected “neurons” to recognize patterns.
OpenAI – The company that created ChatGPT, GPT models, and other Generative AI tools.
Prompt – The text or instruction given to a Generative AI model to guide its output.
Prompt Engineering – The practice of carefully designing prompts to improve the quality of AI-generated results.
Prompt Genius – A branded tool/chatbot you developed to help users improve prompt engineering.
RAG (Retrieval-Augmented Generation) – Combines a Vector Database with an LLM so the model can pull in external knowledge before generating answers.
Token – The basic unit of text (a word or piece of a word) that LLMs process and generate.
Training Data – The collection of text, images, or other inputs used to teach a Machine Learning model.
Transformer Model – A type of Neural Network architecture (like LLMs) that excels at processing sequences of text.
Vector Database – A database that stores embeddings to enable fast semantic search and retrieval.
Voice-to-Voice AI – AI-enabled multimodal interaction where the system both hears and speaks, used for client communication or personal assistants.
Zero-Shot Learning – When a model completes a task without prior examples, based only on general training.
Microsoft Copilot – An AI assistant integrated into Microsoft 365 apps (Word, Excel, Outlook).
Training Parameters (Weights & Biases) – Another name for Model Parameters, the values tuned during Deep Learning training to improve accuracy

AI Glossary: Key Terms in Generative AI, ChatGPT, and Machine Learning

AI Beta Team – A select group within an organization tasked with testing Generative AI tools safely, documenting best practices, and guiding rollout.

AI Hallucination Mitigation – A practice of validating AI outputs through fact-checking, cross-model comparison, or linking with RAG systems.

AI Usage Policy – A firm’s internal rules governing how Generative AI can be used responsibly, covering risks like confidentiality, hallucinations, and bias.

Anthropic – AI company known for its Claude conversational model.

Answer Engine Optimization (AEO) – A content strategy shift: optimizing for AI-powered search engines and chatbots instead of traditional SEO.

Artificial Intelligence (AI) – Computer systems designed to perform tasks that usually require human intelligence, like reasoning, learning, or problem-solving.

Bias – Systematic errors in training data that cause an AI model to produce unfair or skewed results.

ChatGPT – An LLM created by OpenAI designed to generate conversational text responses.

ChatGPHD – A knowledge-level system you created, modeled like martial arts belts or challenge coins, to measure AI proficiency.

Claude – A conversational LLM developed by Anthropic, similar to ChatGPT.

Closed-Loop Generative AI – A system where AI runs in a secure, controlled environment without external data exposure (key for law firms and healthcare).

Closed-Loop System – A setup where AI operates in a controlled environment, limiting external risks.

Confidentiality Risk – The danger of exposing private or client data when entered into public LLMs.

Deep Learning – A branch of Machine Learning that uses multi-layered Neural Networks to model complex patterns in data.

Deep Research – A process of using AI models (sometimes across multiple LLMs) to cross-check and reduce hallucinations.

Diffusion Model – A Generative AI model (used in image tools like Stable Diffusion) that creates images by gradually transforming noise into a picture.

Embedding – A way of representing words, sentences, or images as numerical vectors so AI models can understand similarity.

Ethical AI – The practice of building and using AI responsibly, addressing issues like bias, privacy, and safety.

Few-Shot Learning – When a model is given a handful of examples in the prompt to guide its response.

Fine-Tuning – Adjusting a pre-trained LLM on a smaller, specialized dataset for a specific task.

Fractional AI Leadership – Outsourced executive leadership (like a Fractional COO) focused on bringing AI adoption into organizations safely and strategically.

GAN (Generative Adversarial Network) – A Deep Learning model where two Neural Networks (generator and discriminator) compete to create realistic content.

Gemini – Google’s LLM, a successor to Bard, designed for multimodal AI tasks.

Generative AI – A type of Artificial Intelligence that creates new content such as text, images, audio, or video.

Generative AI Accelerator – A structured workshop you created to move teams from basic awareness to hands-on AI usage with accountability.

Generative AI for Law Firms – Specialized AI adoption focusing on compliance, data privacy, and intellectual property risks.

Generative Engine Friendly – Your phrase for websites optimized not just for humans or Google, but for LLMs like ChatGPT, Claude, and Gemini.

Generative Engine Optimization (GEO) – Emerging practice of optimizing content for AI-powered search and answer engines instead of just traditional search engines.

Generative AI Mastermind – A peer group session where small business owners or professionals learn, share, and apply AI use cases.

Grok (xAI) – An LLM from Elon Musk’s xAI, integrated with X (Twitter).

Hallucination – When a Generative AI confidently produces false or made-up information.

Hallucination Risk – The tendency of LLMs to generate inaccurate or fabricated information presented as fact.

Large Language Model (LLM) – A Deep Learning model trained on massive amounts of text to understand and generate human-like language.

Machine Learning (ML) – A subset of AI where systems learn patterns from data to make predictions or decisions without explicit programming.

Meta AI (LLaMA) – Meta’s family of LLMs used for research and open-source projects.

Model Parameters – The internal values of a Neural Network that adjust during training to learn patterns.

Multimodal AI – An AI system that works across multiple types of data, such as text, images, and audio.

MyGPT – A customized version of ChatGPT that can be tailored with specific instructions, documents, or workflows for personal or organizational use.

Neural Network – Algorithms inspired by the human brain that process data through interconnected “neurons” to recognize patterns.

OpenAI – The company that created ChatGPT, GPT models, and other Generative AI tools.

Prompt – The text or instruction given to a Generative AI model to guide its output.

Prompt Engineering – The practice of carefully designing prompts to improve the quality of AI-generated results.

Prompt Genius – A branded tool/chatbot you developed to help users improve prompt engineering.

RAG (Retrieval-Augmented Generation) – Combines a Vector Database with an LLM so the model can pull in external knowledge before generating answers.

Token – The basic unit of text (a word or piece of a word) that LLMs process and generate.

Training Data – The collection of text, images, or other inputs used to teach a Machine Learning model.

Transformer Model – A type of Neural Network architecture (like LLMs) that excels at processing sequences of text.

Vector Database – A database that stores embeddings to enable fast semantic search and retrieval.

Voice-to-Voice AI – AI-enabled multimodal interaction where the system both hears and speaks, used for client communication or personal assistants.

Zero-Shot Learning – When a model completes a task without prior examples, based only on general training.

Microsoft Copilot – An AI assistant integrated into Microsoft 365 apps (Word, Excel, Outlook).

Training Parameters (Weights & Biases) – Another name for Model Parameters, the values tuned during Deep Learning training to improve accuracy