How do LLMs Really Think?

Welcome to the Property AI Tools Newsletter!

Today I’ll be exploring:

• A breakdown of how language models actually ‘think’

• How LLMs handle reasoning and chain of thought

• Why some language models refuse requests

• What language models do not do

For real estate professionals, understanding how these models “think” matters.

They're now supporting property searches, tackling client questions, and even helping with contract analysis. Knowing what goes on behind the scenes can help you use these tools with more confidence and spot their strengths and limitations when dealing with buyers, sellers, and legal documents.

What Are Large Language Models?

Large language models (LLMs) work like super computers that soak up information from countless books, websites, and documents. They pay attention to patterns and relationships in how people talk and write, and this helps them to respond with sentences that feel natural and relevant.

Let’s take a look at their core purpose; predicting what comes next in a conversation or document based on what they've seen before.

What Makes Up a Large Language Model?

At the heart of an LLM is a deep neural network, the brain of the system. Instead of wires and neurons (like a human brain), it uses numbers and mathematical weights to represent patterns in language.

Key points to remember about LLMs:

Training Data: They study massive text collections (from books, articles, websites) to build a wide knowledge base.

Token Processing: LLMs break down everything you type into small chunks called tokens, like words or even just parts of words.

Prediction: The main job is to guess the next best word or phrase in a sentence based on what’s already been said.

Fine-Tuning: Sometimes, models are further adjusted using specialised data to suit specific use cases e.g. property listings, contracts, or FAQs unique to real estate.

If you’re interested in learning more about prompt engineering and the best methods for instructing an LLM, read my article on how to Master Real Estate Tasks with Prompt Engineering.

How LLMs “Learn”

Rather than memorising text, LLMs learn through patterns. While reading huge amounts of text, they pick up on:

Context: What words work together and how meaning shifts with phrasing.

Intent: Recognising the purpose behind a question or statement.

Style: Adapting tone and detail, whether it’s a casual chat or a formal contract.

LLMs are data ‘reproducers’ and given the right input, they can generate content that meets your needs, whether you’re drafting an email, reviewing client communications, or checking property descriptions.

Pattern Recognition and Prediction in Action

Every time you type a question or a request a language model looks at that phrase as a string of clues. Instead of seeing words, it sees data points it learned from thousands of similar questions.

Here’s how it works in practice:

Spotting Intent: If you write, “I need a large garden for my kids” the model connects your need with listings that highlight family-friendly gardens.

Predicting Next Steps: When you ask, “What schools are nearby?” after searching for a house, the model recognises school proximity as a top factor for buyers and brings up school information in its answer.

Responding to Trends: Over time, the model gets better at connecting your wording to what other users want. For example, frequent questions about parking or broadband speed mean those replies are always relevant.

By matching your request to familiar phrases and outcomes, it replies with suggestions that feel tailored to your needs, no guesswork involved.

Multi-Step Reasoning and Chain of Thought

Some property queries aren’t solved with a quick answer. You might want to compare two properties, weigh up several investment options, or balance features like price, location, and condition. This is where language models shine at stringing thoughts together in a logical order.

For instance:

• Comparing Features: You ask, “Which property is better for a growing family. The terraced home near the park or the semi-detached closer to schools?” The model lists pros and cons for each, such as garden size, space for prams, or school catchment areas.

• Weighing Investment Value: You’re looking at two buy-to-let flats. The model can help you list current rent prices, future value predictors, and local market trends to make a reasoned summary.

• Step-by-step Problem Solving: Suppose you want to renew a lease but have specific concerns. The model can walk you through steps: reviewing the document, flagging legal points, and outlining your next move.

In each case, the model doesn’t jump straight to an answer. It breaks down the problem into parts, tackles each step in order, and shows its reasoning. If you’re interested in learning more about language models that have been primed and tailored for specific use cases, check out my SLM vs LLM article exploring the benefits of small language models.

By processing your queries this way, language models help you see options clearly and move forward with confidence. They give you a way to think through details, much faster.

Limits and Abilities

Large language models can tackle a wide range of questions, but they have their boundaries. When you rely on these tools to help with property searches or legal details, it's key to know what they can and cannot answer confidently. This protects your business, your clients, and supports trust in every conversation.

Why Language Models Sometimes Refuse Requests

You might notice that sometimes language models refuse to answer certain questions or act on specific requests. This isn’t a bug or a gap in their learning, it’s a built-in safety and compliance measure.

Here’s what guides these refusals:

1. Ethical Boundaries
   Models are trained with rules that stop them from giving advice that could be unethical or break the law. For example, they won’t draft a contract that leaves out legal protections or tell someone how to bypass property regulations.

2. Legal Compliance
   Language models won’t help with requests that involve sharing private data, promoting discrimination, or offering financial advice that needs a licensed professional. This covers issues from GDPR to avoiding bias in tenant selection.

3. Safety for All Parties
   If you ask for content that promotes risky behaviour, like advising on unsafe building practices, the model will either politely refuse or redirect you to seek help from a qualified expert.

Just as you follow best practices and legal standards in property transactions, the model sticks to rules that defend fairness, legality, and responsible service.

What Language Models Do Not Do: Myths and Limits

Understanding what large language models cannot do is just as important as knowing how they work. It’s tempting to treat these tools as all-knowing digital assistants, but they have real boundaries. Many myths still persist around their abilities. If you rely on LLMs for property advice, document creation, or client support, knowing these limits keeps your expectations clear and your service professional.

Myth 1: Language Models “Understand” Like People

In reality LLMs don’t “think” or “know” in the way you do. They spot patterns in text and predict responses that seem sensible based on training data.

• No personal opinions: The model doesn’t believe or prefer anything. It just infers based on it’s training data.

• No emotions or intentions: You’re not speaking with a person, but with a very advanced calculator for words.

• Surface-level patterns: It recreates common answers but doesn’t connect to real-world experience or context outside its training.

Myth 2: All Information Provided is Current and Accurate

Language models don’t have access to the latest property listings or updated laws. Their training ends at a set time, and they don’t browse the web like you do.

• Out-of-date facts: A model might reference rules, rates, or properties that are no longer relevant if not regularly monitored and updated.

• Limited to its dataset: If it hasn’t “seen” a new regulation, feature, or local trend, it won’t mention it.

• No real-time updates: For property availability, market trends, or legal changes, always double-check using trusted sources.

Myth 3: Language Models Can Reason With Deep Understanding

Models can mimic multi-step reasoning, but they don’t truly “think through” problems.

• No actual decision-making: The model doesn’t weigh options or make choices like a human agent.

• Lacks real-world judgement: It can outline pros and cons, but it doesn’t judge which risk or benefit matters most in practice.

• Mistakes with complexity: On tricky legal or investment topics, it might sound sure without grasping complications or exceptions.

Myth 4: They Remember Previous Conversations

Some LLM-powered tools claim they “remember” chats, although partially true, it is usually limited or simulated.

• Short memory span: In one session, they might track recent exchanges but forget context as the conversation grows.

• No true long-term memory: Each chat is separate, with no link to your last session or unique business context.

• No learning from your work: Unless specially designed, models do not remember your clients, listings, or agency preferences.

Always give details as if you’re speaking to a new assistant.

Conclusion

Now you have a good understanding of how language models work, I challenge you to do some stress testing of your own. Create a list of questions to ask the LLM on various topics including gender, equality and politics to see what feedback you receive. It’s the best way to understand the actual boundaries and limits that have been put in place to protect users from adverse interactions.

Remember, AI can save you time but should never replace your own review process and seal of approval. It’s still important to treat AI answers as a starting point. Always ensure to cross-check property data, legal advice and trusted sources to confirm information before sharing it with a client. Never send out legal documents generated by AI without proof reading, your personal judgement adds a level of trust and validity no language model can match.

Stay curious, keep testing AI tools, and update your own knowledge as AI develops. This mix will keep your standards high and positions you as the most knowledgeable real estate professional in the room.

Thanks for reading!

Danielle 

Property AI Tools Founder | AI Consultant @ Caique