AI Reality Check: Glossary
Useful Terms To Know When Discussing AI
A NextAgency Resource
Last Updated: April 16, 2026
If you are looking to incorporate AI into your life and health agency, these are terms you’ll want to know.
This Glossary is in support of the seven-part NextAgency AI Reality Check series discussing the potential and perils of incorporating AI into your life and health insurance agency.
A. Core AI Concepts
A1. Artificial Intelligence (AI): Software that performs tasks typically requiring human intelligence, like drafting emails, analyzing policies, or suggesting cross-sells.
A2. Generative AI: A category of AI that creates new content—text, images, audio, or video—in response to prompts, rather than simply retrieving or organizing existing information. Large language models like ChatGPT and Claude are generative AI. The term appears constantly in vendor marketing, often interchangeably with “AI,” though not all AI is generative.
A3. Hallucination: When AI generates false or misleading information that sounds correct. Hallucinations can include fake plan names, incorrect dates, or made-up summaries. They’re a natural result of prediction-based outputs. Humans often call this AI lying. Hallucinations must be caught by human review—especially in regulated environments.
A4. Large Language Model (LLM): AI models that generate text through pattern matching and statistical prediction—not true understanding—based on massive training data.
A5. Machine Learning: AI that detects patterns in data to improve over time—for example, suggesting follow-ups before renewals. This term is used less frequently today as machine learning is now generally assumed to be a standard feature of AI rather than a distinct capability.
A6. Prompt: A written instruction given to the AI to produce a response. Prompts can be questions, requests, or commands—and clarity strongly affects the quality of the output.
A7. Token: Tokens are the basic unit AI uses to process text. AI breaks text into chunks called tokens—roughly 4 characters each, including spaces and punctuation. “Dental plan” is two tokens; “$1,234.56” might be five. Token limits affect how much you can input, how long replies can be, and how usage is typically billed by AI services.
A8. Training Data: The large dataset—usually public internet content—that a base AI model learns from during development. This data is fixed and not updated by user uploads or individual conversations.
B. Technical Settings & Capabilities
B1. API (Application Programming Interface): A standardized way for different software programs to communicate and share data with each other. APIs allow AI tools to integrate with your existing systems—like pulling client data from your CRM or sending messages through your email platform—enabling automation within your current workflow rather than requiring separate logins or manual data transfer.
B2. Context Window: The maximum amount of text (measured in tokens) that an AI model can process and remember within a single conversation session. When you exceed this limit, the AI loses track of earlier parts of the conversation, potentially missing important context like client details mentioned at the start. Context windows vary greatly by model and between AI platforms.
B3. Fine-tuning: The process of retraining an AI model on specialized data to improve its performance in a specific domain. Fine-tuned models generate responses that reflect the patterns and language of the training material. True fine-tuning is rare—most tools just use pre-written prompts to sound industry-specific.
B4. MCP (Model Context Protocol): An open standard that allows AI models to connect directly with external software systems—like your CRM, calendar, or document library—so they can read from and write to those systems in real time. MCP is the technical backbone behind many AI agent and copilot features.
B5. Model Version: Different releases or iterations of AI systems from the same company, each with distinct capabilities and performance characteristics. Some are trained to handle longer inputs, follow more complex instructions, or generate more creative responses than others. As a result, different models from the same company can be better at different kinds of tasks.
B6. Multimodal: AI that can read and respond to more than just typed questions. You can upload a PDF, spreadsheet, or image—and ask the AI to summarize, extract details, or answer questions about it.
C. Advanced Features and Safety
C1. Agentic Workflows: A sequence of tasks executed automatically by AI agents—retrieving information, making decisions, and taking actions across connected systems without requiring human input at each step. Workflows are executed by agents. Agentic workflows can meaningfully reduce repetitive work, but a misconfigured one can execute the wrong actions at scale before anyone notices.
C2. AI Agents: AI-powered tools that take actions—updating records, sending emails, scheduling appointments, or executing multi-step workflows across connected software—rather than simply responding in a chat window. Agents execute workflows. These capabilities can be convenient, but create real privacy and security concerns in regulated industries, particularly because actions are often taken without human oversight.
C3. Confidence Score: An internal probability rating AI assigns to each word or phrase it generates, reflecting how statistically likely that choice is based on its training. These scores are hidden from users, which means AI presents every response with equal apparent certainty—whether it’s reliably correct (high score) or a mere guess (low score). It is this presentation of uncertain information with great confidence that can make hallucinations difficult to spot without independent verification.
C4. Grounding: The practice of linking AI output to specific, verifiable sources. Grounded responses cite materials like regulations, plan summaries, or official handbooks, making them easier to verify and more reliable for compliance-sensitive tasks.
C5. Guardrails: Built-in safety limits designed to prevent AI from responding to harmful, unethical, or noncompliant requests. Guardrails restrict topics like medical diagnosis, legal advice, and certain financial guidance. Their effectiveness varies significantly by vendor and product—and they are not a substitute for staff training or internal review policies.
C6. Guardrail Bypass (Jailbreaking): Prompts—intentional or accidental—that cause AI to override its safety limits, typically by rephrasing a restricted request in a way that evades detection. Also called jailbreaking. Because guardrails are software rules rather than hard constraints, they can fail. Agencies using AI for client-facing or compliance-sensitive tasks should treat guardrails as one layer of protection, not the only one.
D. Terms You’ll Encounter
D1. AEO (Answer Engine Optimization): The AI equivalent of Search Engine Optimization (SEO), helping websites get cited or summarized by AI-powered search tools. Where SEO focuses on getting pages to appear in a list of links, AEO focuses on getting content used as the actual answer. A related term, GEO (Generative Engine Optimization), refers specifically to optimizing for AI systems that generate original responses rather than returning search results.
D2. AI Copilot: A marketing term used by software vendors to describe an AI assistant embedded directly in their product designed to answer plain language questions about your data. The term is occasionally used as a brand name by companies for their AI, which can be confusing.
D3. Deterministic vs. Probabilistic: Deterministic systems produce the same output every time given the same input—like a calculator. Probabilistic systems, like AI language models, generate responses by predicting the most likely next word or phrase, one step at a time. Since multiple valid responses usually exist, the same prompt can produce different results each time. This is why AI output requires human review rather than blind trust.
D4. Human in the Loop: A workflow in which a human reviews, approves, or corrects AI output before it’s used or sent. In vendor marketing, this phrase often implies a safety feature—but it’s only meaningful if the review step is actually built into your process. An AI tool that can send client emails without review has no human in the loop, regardless of what the brochure says.
D5. Prompt Engineering: The practice of crafting AI instructions to improve the quality and accuracy of responses. Well-designed prompts specify the task, the audience, the format, and any constraints. The difference between a vague prompt and a precise one can be the difference between a usable output and one that requires significant editing.
D6. Retrieval-Augmented Generation (RAG): A method that combines AI text generation with live document search. Instead of relying solely on its training data, a RAG-enabled AI retrieves relevant content from connected sources—like plan documents or uploaded files—before generating a response. This reduces hallucinations and makes answers more verifiable.
Learn More:
Access the NextAgency’s AI Reality Check for Life & Health Insurance Agency series in the Resources Center.
Author Information:
The AI Reality Check series is the work of Alan Katz, NextAgency co-founder, and two artificial intelligence models: OpenAI’s ChatGPT and Anthropic’s Claude.