There’s a lot of jargon surrounding artificial intelligence. Before we dive into the substance of AI and what it means to insurance agencies, let’s clarify some of it. Here’s some of the more common terms you’ll encounter in this series of posts and as you explore AI for yourself. The definitions were provided by ChatGPT and Claude AI. So if they’re wrong, please take it up with them.
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. Machine Learning (ML) AI that detects patterns in data to improve over time—for example, suggesting follow-ups before renewals.
A3. Large Language Model (LLM) AI models that generate text through pattern matching and statistical prediction—not true understanding—based on massive training data.
A4. Deterministic vs. Probabilistic Deterministic systems produce the same output every time when given the same input—like a calculator. Probabilistic systems, like AI language models, don’t follow predetermined rules. They generate responses by predicting the most likely next token (a word or piece of a word), one at a time. Since there are usually many valid next tokens, the same prompt can produce different responses.
A5. 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.
A6. 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.
A7. 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.
A8. 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 and must be caught by human review—especially in regulated environments.
B. Technical Settings & Capabilities
B1. 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.
B2. 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.
B3. 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.
B4. 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.
B5. Temperature Controls how predictable or creative AI responses are. Low settings give safer, more factual answers—useful for client emails or compliance content. High settings generate more variety, but increase the risk of errors or off-topic replies.
B6. 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.
C. Advanced Features and Safety
C1. Retrieval-Augmented Generation (RAG) A method that combines AI text generation with live document search. Instead of relying solely on training data, RAG retrieves relevant content from connected sources—like plan documents or uploaded files—and uses it to generate grounded responses.
C2. 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.
C3. AI Agents AI systems designed to autonomously plan and execute multi-step tasks without step-by-step human guidance. Unlike simple AI that responds to single prompts, agents can break down objectives, sequence actions, and interact with multiple software systems to complete workflows. Truly autonomous agents don’t exist yet in reliable, production-ready forms.
C4. Confidence Score Internal probability ratings that AI systems assign to each word or phrase they generate, reflecting the statistical likelihood of that choice based on training patterns. These scores remain hidden from users, meaning AI presents all responses with equal apparent certainty regardless of the underlying confidence level.
C5. Guardrails Built-in safety limits that prevent AI from answering dangerous, unethical, or noncompliant prompts. These include restrictions on medical, legal, and financial advice. Guardrails are shaped by vendor policies and background system instructions that influence tone and scope.
C6. Guardrail Bypass Prompts—intentional or accidental—that cause AI to override its safety rules, often by rephrasing restricted requests. Bypass attempts can expose users to compliance or legal risks if protections are weak or misconfigured.
In the next post we’ll explore what AI can do for health insurance agencies and in general.