Make sure you understand these AI basics.
Samantha Stark is founder and chief strategist at Phyusion.
Foundational AI concepts
Artificial intelligence (AI) — A broad field of computer science devoted to building machines that can perform tasks that normally require human intelligence, such as perception, reasoning, learning, and language use.
Agentic capabilities — The capacity of an AI system to pursue goals autonomously: making decisions, planning, and acting with minimal or no human intervention while respecting predefined constraints.
Generative AI — Models that learn patterns in data and can create new content—text, images, audio, or video—that resembles the training distribution when given a prompt.
AEO (answer engine optimization) — The practice of optimizing content and prompts to improve visibility, relevance, and performance within generative AI systems like ChatGPT, Gemini, or Claude.
Large language model (LLM) — A large-scale neural network trained on massive text corpora to understand and generate human-like language (e.g., GPT-4, Claude 3, Gemini 5).
Transformer architecture — A deep-learning model introduced in 2017 that uses self-attention to process entire sequences in parallel, forming the technological backbone of modern language, vision, and multimodal models.
Machine learning (ML) — A subfield of AI that enables computers to learn patterns from data and improve at a task over time without being explicitly programmed with task-specific rules.
Natural language processing (NLP) — The branch of AI focused on enabling computers to understand, generate, and interact using human language.
Voice cloning — Techniques that analyze a speaker’s vocal characteristics and synthesize speech that mimics their voice, enabling personalized or consistent audio content.
Contextual awareness — An LLM’s ability to incorporate conversation history, user-specific data, or retrieved documents to ground its outputs in relevant context.
Multimodal generation — Creating or interpreting content across more than one modality (e.g., combining text, images, audio, or video in a single model interaction).
Microsoft Copilot–specific terms
Microsoft 365 Copilot — An AI assistant embedded across Microsoft 365 apps (Word, Outlook, PowerPoint, Excel, Teams, etc.) that helps draft content, summarize information, analyze data, and automate workflows.
Enterprise-grade security — Microsoft 365 Copilot inherits the platform’s compliance, privacy, and security controls (encryption, identity, data residency, admin governance) to protect company data and IP.
BizChat — A Copilot feature that uses natural-language queries to pull insights from across Outlook, Teams, OneDrive, and SharePoint—summarizing meetings, emails, and documents into useful updates.
Common AI tools
ChatGPT — OpenAI’s conversational interface for GPT models, capable of answering questions, generating content, and assisting with a variety of tasks.
Custom GPT — Tailored versions of OpenAI’s GPT models that organizations or individuals configure with specific instructions, proprietary knowledge, and tool integrations to solve domain-specific problems.
Claude — Anthropic’s AI assistant noted for its constitutional-AI alignment approach, large context windows, and nuanced reasoning.
Claude projects — A workspace feature in Claude that lets users upload documents and collaborate with the model across iterative sessions—functionally similar to a custom GPT.
Gemini — Google DeepMind’s multimodal AI model (formerly Bard) available in Google Workspace and other Google products.
DALL-E — OpenAI’s text-to-image model that generates original images from natural-language descriptions.
Midjourney — An independent generative-image tool widely used for producing high-detail artwork from text prompts.
Sora — OpenAI’s text-to-video model that generates short, realistic, and imaginative video clips from textual prompts.
HeyGen — A platform for creating AI-generated videos featuring customizable digital avatars and voiceovers.
ElevenLabs — Advanced AI voice technology for natural-sounding speech synthesis and speaker-specific voice cloning.
Perplexity — An AI search and question-answering engine that combines large-scale retrieval with LLMs to deliver cited, concise answers.
RunwayML — A generative AI tool for video creation and editing, including text-to-video generation, video-to-video generation, and image animation.
Prompt engineering basics
Prompt — The textual (or multimodal) input provided to an AI model to elicit a desired response.
Prompt engineering — The craft of designing, structuring, and iteratively refining prompts to guide AI systems toward high-quality, task-relevant outputs.
Prompt templates — Reusable prompt structures that encapsulate best practices and can be rapidly adapted for different tasks.
Temperature — A generation parameter (0–1 or 0–2 depending on the model) controlling randomness; higher values yield more diverse and creative outputs, lower values produce more deterministic results.
Iterative refinement — A workflow in which users review an AI’s output, provide feedback or clarifying instructions, and repeat the process until the response meets quality standards.
AI limitations & considerations
Hallucination — When an AI model produces output that is factually incorrect, fabricated, or nonsensical while sounding plausible.
Training data cutoff — The most recent date of the data used to train a model; events occurring after this date are unknown to the base model unless supplemented by external retrieval.
Token limit — The maximum number of tokens (roughly words or word pieces) that can be processed in a single prompt-plus-response cycle, constraining context length.
Bias — Systematic inaccuracies or unfairness in AI outputs stemming from imbalanced, unrepresentative, or prejudiced training data.
Ethical AI usage — Principles and practices that promote responsible development and deployment of AI, including transparency, accountability, fairness, privacy protection, and mitigation of harmful stereotypes.
Key concepts for corporate PR leaders
Guardrails / policy enforcement — Technical and procedural controls that constrain AI outputs to brand, legal, and compliance standards, preventing the release of disallowed or off-brand content.
Human-in-the-loop (HITL) — A governance workflow where human reviewers approve or amend AI-generated content before publication, ensuring accountability and quality control.
Sentiment analysis — AI techniques that detect and classify emotional tone in text or speech, enabling real-time monitoring of audience reactions and message impact.
This article is a preview of content available to members of Ragan’s Center for AI Strategy.
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