Generative AI is a type of artificial intelligence that can create new content — such as text, images, video, or audio — based on patterns it has learned from training data.

An LLM is a type of AI model trained on massive amounts of text to understand, predict, and generate human-like language.

AI is the broad field of building intelligent machines. Machine Learning is a subset where models learn patterns from data. Deep Learning is a branch of machine learning that uses layered neural networks to handle complex tasks.

ChatGPT processes text as tokens, predicts the most likely next word, and builds responses one token at a time based on its training data and model parameters.

They’re all LLMs but built by different companies: OpenAI (ChatGPT), Anthropic (Claude), Google (Gemini), and xAI (Grok). Each has unique strengths in safety, speed, multimodality, or integration.

A prompt is the instruction or question you give to a Generative AI tool to guide its response.

Prompt engineering is the practice of designing and structuring prompts to improve the accuracy, creativity, or usefulness of AI responses.

A MyGPT is a personalized version of ChatGPT that can be customized with your documents, workflows, and rules for tailored use.

Typical use cases include drafting emails, summarizing documents, financial forecasting, marketing copy, customer service chatbots, legal research, and data analysis.

Small businesses can save time (often 30%+), reduce costs, improve client communication, and scale operations by using AI for templates, automation, and decision support.

A hallucination happens when an LLM generates information that sounds correct but is factually false or fabricated.

Use RAG (Retrieval-Augmented Generation), fact-check with multiple sources, or compare answers across different models (Deep Research).

Not in public versions. Entering sensitive data can create a confidentiality risk. Many organizations use closed-loop systems or enterprise AI to stay compliant.

Main risks include bias in training data, hallucinations, copyright issues, deepfakes, and breaches of confidentiality.

Bias leads to skewed or unfair results because the training data may not represent all groups fairly.

An AI Beta Team is a pilot group inside a company that experiments with Generative AI, tests safe use cases, and helps guide firmwide adoption.

To clearly define permitted uses, protect client data, reduce hallucination risk, and meet compliance requirements.

It means designing websites and content so they’re easily understood and cited by LLMs like ChatGPT, Claude, and Gemini — not just by Google search.

AEO is optimizing content for AI-powered search engines and answer bots, the next evolution beyond SEO.

It helps with drafting, legal research, summarizing documents, and workflow automation, but must be used under strict confidentiality and compliance guidelines.

Learn how you can stay ahead with the ChatGPT 101 Accelerator.

Fine-tuning is adapting a general model by training it further on your organization’s specific data.

A vector database stores embeddings (numerical representations of text/images) to enable fast, semantic search in AI applications.

RAG connects an LLM with a vector database so the AI can pull in real information before generating an answer.

Multimodal AI can process and generate across different data types (text, images, audio, video).

GANs use two neural networks competing to create realistic outputs, while Diffusion Models (like Stable Diffusion) generate images by refining random noise into pictures.

Legal, healthcare, finance, marketing, education, and manufacturing are leading adopters.

Always review, fact-check, and validate results — AI should be a starting point, not the final answer.

It’s more likely to reshape jobs by automating repetitive work, freeing people to focus on higher-value tasks.

Start with low-risk tasks (drafting, summarizing, brainstorming), join an AI Beta Team, and always review outputs for accuracy.

We offer Generative AI 101 Training Sessions to prepare your team to adopt AI safely and effectively.

Follow major model releases (OpenAI, Anthropic, Google, Meta, Microsoft), join professional communities, and test new tools regularly.