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.
Approved Tools: AI systems authorized for use by a company.
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.
Confidential data - Sensitive company or client information that must be protected.
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.
PII ( Personally Identifiable Information) - Data that can identify an individual, such as names, SSNS, OR ADDRESSES.
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.
Proprietery information - Company-owned intellectual property or trade secrets.
RAG (Retrieval-Augmented Generation) – Combines a Vector Database with an LLM so the model can pull in external knowledge before generating answers.
Shadow AI - Employee use of unapproved tools.
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
Generative AI: AI that creates text, images, audio, or other content.
Shadow AI: Employee use of unapproved AI tools.
Confidential Data: Sensitive company or client information that must be protected.
Proprietary Information: Company-owned intellectual property or trade secrets.
Approved Tools: AI systems authorized for use by a company.
Hallucinations: Incorrect or fabricated AI outputs that sound convincing.
Large Language Model (LLM): AI trained on vast text datasets to generate language.
Prompt: The instruction given to an AI to generate output.
Training Data: Information used to teach AI models how to respond.
Token: A unit of text AI uses to process inputs and outputs.
Bias: Systematic error or unfairness in AI outputs.
Data Privacy: Protection of personal or sensitive information.
PII (Personally Identifiable Information): Data that can identify an individual, such as names, SSNs, or addresses.