**Written by: ****Matt Halloran**

You've said "agentic AI" in a meeting this month. Could you actually define it? Be honest. Most advisors can't.

Nobody's fault. Vendors throw these words around expecting you to nod along. So let's fix that. Ten terms you keep hearing. Here's what they actually mean for your practice.

1. Hallucination. This is when AI just makes stuff up. Confidently. Like it's reading you a fact. It's not broken when it does this; it's just how these models work. So never let a number an AI spits out touch a client's plan without your eyes on it first. If a vendor tells you their AI "doesn't hallucinate," walk away. They're lying, or they don't understand their own product. You are not perfect; neither is your AI.

2. RAG. Stands for Retrieval-Augmented Generation; forget that, here's what matters. Is the AI actually looking at your client's file, or is it just guessing based on what sounds right? That's the whole question. Ask every vendor this directly. One version you can trust with real client details. The other one, you can't. This is why two-way integrations are so important for advisors.

3. Context window. Think of this as the AI's short-term memory. Small window, it forgets what you told it five minutes ago. You've felt this. You're mid-conversation, and it suddenly acts like you never gave it that context. That's the window running out. Ask how big it is before you build your workflow around a tool.

4. Agentic AI. This is the one everyone name-drops, and almost nobody has. (Unless you built one or buy one that is built by a professional.) A chatbot answers your questions. Agentic AI does the work. Books the meeting. Drafts the follow-up. Updates the CRM without you asking twice. If a tool can only talk to you, it's not agentic, no matter what the sales deck says.

5. Fine-tuning. Take a general AI, ChatGPT, for example, and train it specifically on your world. That's fine-tuning. A generic model gives you generic advice that sounds smart but misses the point. A fine-tuned one actually understands suitability, fiduciary language, how you talk to clients. Ask if the tool was built for advisors or just bolted onto something built for everybody.

6. Prompt engineering. I hate how intimidating this sounds because it's just asking better questions. Vague question, vague answer. Every time. The advisors winning with AI right now aren't smarter than you. They're just more specific. By the way, you can ask your AI to make your prompt better, and it will rewrite it to make it work better- super cool.

7. Token. The chunks AI breaks your words into to process them. Why does this matter to you? It's why some tools charge you by usage or choke on a long document. Next time a pricing model feels random, this is usually why. More tokens, more help.

8. Temperature. Controls how predictable or "creative" the AI's answer is. Low for compliance language; you want it boring and accurate. Higher when you're brainstorming content ideas. If a tool gives you a different answer every time you ask the same question, temperature is probably why. Tell it the temperature; this is where projects work really well in AI. You can preset the temperature for each project, and it should remember.

9. LLM. Large Language Model. This is the engine under basically everything you're calling AI right now: ChatGPT, Claude, all of it. Here's why you should care. Most platforms aren't building their own intelligence. They're building on top of the same handful of models. The real value is in what they build on top.

10. Multimodal. AI that can handle text, images, and audio, not just typed questions. Practically, this means a tool that can actually read a screenshot of a client email or a scanned document and do something with it. Not just read it back to you.

You don't need a computer science degree for any of this. You need to know what you're actually buying.