A no-fluff glossary for real estate pros navigating the AI wave

If you’ve been in any brokerage strategy meeting, attended a proptech webinar, or just scrolled LinkedIn lately, chances are you’ve heard someone drop terms like “LLM,” “RAG,” or “fine-tuning.”

You might sort of know what they mean. But let’s be real—most of us are nodding along while silently Googling.

This glossary is your cheat sheet. Whether you’re testing AI tools in your business, exploring automations, or just trying to sound smart in a meeting, here’s a simple breakdown of the most common AI lingo.

Model

An AI model is the “brain” behind any AI tool.
Give it input (like a listing description), and it gives you output (like a rewritten version tailored to first-time buyers).

In real estate, models might help you write property copy, generate follow-up emails, or analyze lead data.
They’re what make the magic happen.

LLM (Large Language Model)

These are models trained to understand and generate human-like text.
ChatGPT, Claude, Gemini—all LLMs.

They’re useful in real estate for everything from writing client newsletters to generating training materials for your team.

Transformer

The transformer is the architecture that makes these smart tools work.
It lets models process all the words in a sentence at once instead of one-by-one—so they can understand nuance and context.

This is why AI-generated listing descriptions today sound human, not robotic like they did five years ago.

Training / Pre-training

Training is when the AI learns—by reading billions of documents, articles, books, and websites.
For LLMs, it’s about predicting the next word in a sentence.

You don’t do this yourself (OpenAI and others handle that part), but knowing it helps you understand why AI is good at general tasks but not always hyper-specific to your business.

Tokens

AI doesn’t see full sentences—it sees tokens.
A token is just a piece of a word or punctuation.

Why it matters: Most AI tools are priced or limited based on how many tokens you use, not how many words.

Fine-tuning

This is when someone takes a general model and “trains” it on more specific data.

In real estate, this might mean fine-tuning a model on thousands of agent bios, or client inquiries, to create better assistant tools, onboarding flows, or support bots.

RLHF (Reinforcement Learning from Human Feedback)

After the base model is trained, it can be improved by showing it human preferences.
For example, if a model generates two responses to “How do I prepare my home to sell?”—a human evaluator can pick the better one. The model then learns from that.

This process helps the model become more aligned with what people actually want to hear. Especially important in high-empathy industries like real estate.

Prompt Engineering

The art of asking the right question.
How you prompt the model affects the quality of the answer.

Prompt: “Write a listing for a 3-bedroom home in Denver.”
Better prompt: “Write a warm, engaging listing description for a 3-bedroom bungalow in Denver, geared toward growing families looking for walkability and great schools.”

You don’t need to be technical to be great at this—it’s about clarity and context.

RAG (Retrieval-Augmented Generation)

Sometimes, the model needs more information than it was trained on. RAG allows it to pull in external documents or data at run-time.

Imagine a coaching assistant that, when asked “How many homes closed in my area last month?” could pull directly from your MLS report or brokerage dashboard.

That’s RAG in action—answering based on real-time info instead of memory.

Inference

Inference is just the moment the AI runs.
When you type a prompt and hit enter, and it spits something out? That’s inference.

Evals

Short for evaluations.
Evals are how AI builders test whether a model is doing what it’s supposed to do—accurately, respectfully, and consistently.

If you’re choosing an AI tool for your business, ask vendors what evals they’ve run. You want to know they’ve tested for things like tone, hallucination, and bias.

Supervised vs. Unsupervised Learning

• Supervised: The AI learns from labeled data (e.g., “This is a condo,” “This is a single-family home.”)
• Unsupervised: It finds patterns on its own, like grouping leads by behavior even if they weren’t pre-labeled.

Multimodal

Many AI tools now accept more than just text.
You can upload images (like listing photos) or even voice inputs.

Useful for future-facing workflows like walk-and-talk listing notes, visual inspections, or even audio-based home tour summaries.

Hallucination

When AI makes something up—but sounds confident.
For example, writing a description of a home with a pool when your listing clearly doesn’t have one.

This is why fact-checking matters. Especially in real estate.

MCP (Model Context Protocol)

An emerging standard that lets AI tools connect to your systems—your CRM, calendar, or MLS—without custom coding.

In the future, MCP may allow AI to automatically log your client calls or follow up with leads based on past interactions.

Quick Recap for Real Estate Use

Term	Why It Matters in Real Estate
Model	It’s the brain behind every AI tool
LLM	Powers your text generation tools
Transformer	Enables realistic, human-sounding responses
Training	Explains why AI has general knowledge
Fine-tuning	Customizes AI for your brokerage or team
RLHF	Makes models more helpful and human-aligned
Prompt Eng.	Helps you get better outputs from tools
RAG	Brings in up-to-date property data or insights
Evals	Ensure the AI tool performs to your standards
Inference	The “answer moment” in any tool
Multimodal	Enables text, voice, and image inputs
Hallucination	Watch out for confident errors

Bottom line?

You don’t need to be an AI engineer to make AI work for your business.
But a little vocabulary goes a long way—especially when you’re choosing tools or training your team.